Electronic fence method for protecting user privacy

Abstract

The invention relates to the technical field of mobile communication, security and differential privacy, and particularly provides an electronic fence method for protecting user privacy, which comprises the following steps: acquiring original position data; and performing localized differential privacy perturbation processing on the original position data to generate perturbation position data satisfying a preset differential privacy budget, the perturbation processing including optimizing noise distribution to maintain subsequent electronic fence determination precision, dynamically binding the dynamic pseudo identity identifier with the current timestamp and the area identification information of at least one target electronic fence area; receiving a fence judgment request from the user terminal equipment; and based on the disturbance position data in the fence judgment request and the area identification information of the corresponding target electronic fence area, executing border crossing state judgment by using a pre-deployed lightweight geo-fence boundary matching algorithm. According to the invention, operation cost and resource consumption are reduced, and system performance and expandability are improved.

Description

Technical Field

[0001] This invention relates to the technical fields of mobile communication, security, and differential privacy, and particularly to an electronic fence method for protecting user privacy. Background Technology

[0002] With the rapid development of mobile internet and location-based services (LBS) technologies, electronic fence technology based on mobile communication networks has been widely applied in areas such as ride-sharing, logistics tracking, child monitoring, and smart security. Traditional electronic fence technology obtains user location in real time through base station positioning (BSP), signal triangulation, or hybrid positioning methods, and dynamically compares it with a preset geofence area to trigger location-related services or alarms. However, existing technologies generally rely on the continuous uploading of user location data and server-side processing, resulting in the long-term exposure of sensitive user location information to communication links and servers, posing a risk of privacy leaks. In recent years, with the frequent occurrence of data breaches and the gradual improvement of user privacy protection regulations (such as the Data Security Law), how to build a full lifecycle protection mechanism for user location privacy while achieving accurate electronic fence functionality has become a key challenge in the development of electronic fence technology.

[0003] Existing geofencing systems face a trade-off between privacy protection and functional performance: on the one hand, frequent location reporting and server processing patterns are easily intercepted by malicious attackers or used to infer user behavior trajectories; on the other hand, simple location obfuscation or encrypted transmission strategies can lead to decreased accuracy in fence boundary determination or service response delays. To address these issues, this proposal suggests a privacy-enhanced geofencing implementation method based on mobile communication technology. By introducing localized differential privacy noise, dynamic pseudo-identity mapping, and a lightweight edge computing framework, location data anonymization and fence logic determination are completed on the terminal side, with only desensitized status trigger signals transmitted to the server. This technology not only meets the real-time requirements of high-precision geofencing services but also effectively prevents the leakage of user location privacy data, providing a compliant and reliable location service infrastructure for smart cities, the Internet of Things, and other fields.

[0004] The existing mobile communication-based electronic fence technology has the following main problems in practical applications: (1) High risk of leakage of user location privacy data: Existing electronic fence technology has the following risks in the data transmission and storage stages; Data link exposure risks: Traditional solutions require continuous uploading of users' original location data (such as latitude and longitude, base station number) to the server for fence determination, resulting in sensitive information being transmitted in plaintext in the communication link, which is vulnerable to man-in-the-middle attacks or illegal theft.

[0005] Server-side storage risks: The server stores users' historical location data for a long time. If the server is attacked or internal personnel violate regulations, it may cause a large-scale privacy breach. Moreover, it is difficult to meet the requirements of the Data Security Law and other regulations for minimizing data storage.

[0006] (2) The contradiction between privacy protection and functional performance: Existing electronic fence technology presents the following contradictions in balancing privacy protection and functional performance; The conflict between accuracy and privacy: Existing encryption or obfuscation techniques (such as generalizing geographic areas or adding random noise) can reduce the accuracy of location data, leading to increased errors in fence boundary determination (e.g., a user may not have actually crossed the boundary but an alarm is triggered falsely), which affects service reliability.

[0007] Insufficient real-time performance: The complex end-to-end encrypted transmission or server-side decryption calculation process increases data transmission and processing latency, making it difficult to meet the requirements of scenarios with high real-time requirements (such as dynamic fence management of shared bicycles).

[0008] (3) Risks related to identity and location: Most systems use fixed user IDs or device identifiers to upload location data. Attackers can combine this with external data (such as social network information) to infer the user's real identity and behavioral trajectory through correlation analysis.

[0009] (4) Insufficient demand coverage: Most existing technologies only focus on encryption during transmission or storage, failing to cover the entire lifecycle of location data collection, processing, and destruction, and thus cannot meet the requirements for data subject rights (such as the right to erasure and the right to withdraw authorization).

[0010] To address the shortcomings of existing technologies, this invention provides an electronic fence method for protecting user privacy. Summary of the Invention

[0011] To achieve the above objectives, the present invention adopts the following technical solution: In one aspect, the present invention provides an electronic fence method for protecting user privacy, comprising the following steps: User terminal device side steps: Obtain raw location data; perform localized differential privacy perturbation processing on the raw location data to generate perturbed location data that meets a preset differential privacy budget. The perturbation processing includes optimizing the noise distribution to maintain the accuracy of subsequent electronic fence determination; generate a dynamic pseudo-identifier for this location report and dynamically bind the dynamic pseudo-identifier to the current timestamp and the area identification information of at least one target electronic fence area; send a fence determination request carrying the dynamic pseudo-identifier, timestamp, area identification information, and perturbed location data to the edge computing node; Edge computing node-side steps: Receive a fence determination request from the user terminal device; based on the disturbance location data in the fence determination request and the area identification information of the corresponding target electronic fence area, perform boundary crossing determination using a pre-deployed lightweight geofence boundary matching algorithm; generate a binary trigger signal that only indicates entry or exit from the target electronic fence area; send the binary trigger signal, dynamic pseudo-identity identifier, timestamp, and corresponding area identification information to the server; Server-side steps: Receives a binarized trigger signal, a dynamic pseudo-identity identifier, a timestamp, and corresponding area identification information from the edge computing node; records, manages, and notifies electronic fence events based on the binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and area identification information.

[0012] In one optional implementation, generating the dynamic pseudo-identity identifier includes generating the dynamic pseudo-identity identifier based on a random number generation algorithm, a hash algorithm, or a combination of the device's own non-identifiable hardware features and timestamps and regional identification information.

[0013] In one optional implementation, the geofence boundary matching algorithm includes at least one of the ray-mapping method and the polygon coverage detection algorithm.

[0014] In one optional implementation, the layered encrypted communication step includes: The user terminal device and the edge computing node use a first encryption algorithm to encrypt the transmission of the fence determination request. The edge computing node and the server use a second encryption algorithm to encrypt the transmission of the binarized trigger signal information.

[0015] In one optional implementation, the server-side steps include an access control step. When the server notifies or accesses data about an electronic fence event, it adopts an attribute-based access control policy to grant or deny the access subject to decryption and / or access rights to the fence event data based on the access subject's attribute information and a preset access policy.

[0016] In one optional implementation, the server-side steps include data lifecycle management steps: After generating and sending a binarized trigger signal, the edge computing node automatically deletes or anonymizes the temporarily stored disturbance location data. The server sets up automatic expiration and deletion or anonymization processing strategies for stored sensitive information such as dynamic pseudo-identifiers; Blockchain technology is used to record the flow logs of data between user terminal devices, edge computing nodes and servers, enabling audit traceability.

[0017] In one optional implementation, the step of using a pre-deployed lightweight geofence boundary matching algorithm to perform the boundary crossing determination includes extracting disturbance location data and the corresponding area identification information of the target geofence area from the received geofence determination request. Based on the area identification information of the target electronic fence area, the digital boundary description information of the target electronic fence area is obtained from the edge computing node locally or from a preset data source in a secure manner. The digital boundary description information may be a sequence of polygon vertex coordinates, the center coordinates and radius of a circle, or a parameterized representation of other regular / irregular geometric shapes. A lightweight geofence boundary matching algorithm pre-deployed on an edge computing node is invoked and executed to determine the spatial relationship between the disturbed location data and the digital boundary description information of the target geofence area. The lightweight geofence boundary matching algorithm includes, but is not limited to: The ray method involves emitting a ray from the disturbance location in any direction and calculating the number of times the ray intersects with the boundary of the electronic fence polygon. If the number of intersections is odd, the point is determined to be within the region; if it is even, the point is outside the region. The polygon coverage detection algorithm determines whether a point is covered by a polygon by calculating the relative positional relationship between the disturbed location point and each side of the polygon, or by using simple area comparison. A fast judgment algorithm for fences of specific shapes is proposed. For circular fences, the distance between the disturbance location point and the center of the circle is calculated and compared with the radius. For rectangular fences, it is determined whether the horizontal and vertical coordinates of the disturbance location point are within the boundary of the rectangle.

[0018] In an optional implementation, the method further includes a step of acquiring historical states and performing state comparison: If it is the first determination or historical status is not required, obtain the current disturbance position relative to the target electronic fence area; If it is necessary to determine an entry or exit event, the previous fence status determination result bound to the dynamic pseudo-identity identifier and the target electronic fence area identification information is retrieved from the local temporary storage of the edge computing node. Compare the current state determination result with the previous state determination result; Steps to generate out-of-bounds status determination results: If only the current state is needed, output the internal or external state; If a state comparison is performed: If the current state is internal and the previous state was external, then it is determined that the state has entered an out-of-bounds state. If the current state is external and the previous state was internal, then it is determined that the state has left the outbound state; If the current state is the same as the previous state, it is determined that there is no out-of-bounds error or the state has not changed.

[0019] In one optional implementation, the server-side steps include receiver preparation and communication link establishment steps: The receiving end is pre-configured with communication parameters for the edge computing node, including: Communication protocol adaptation: Enable the corresponding protocol listening service based on the deployment environment of the edge computing node; Security mechanisms are configured, including loading pre-negotiated encryption algorithms, signature verification public keys, or device identity certificates to ensure the confidentiality and integrity of the communication link; Resource reservation allocates network bandwidth, cache space, and process / thread resources for concurrent data reception from edge computing nodes to avoid reception congestion.

[0020] In one optional implementation, the server-side steps of data reception and preliminary caching are as follows: The receiving end receives data packets sent by the edge computing nodes in real time or periodically through the established communication link, specifically including: Listening and capturing: The protocol listening module captures data packets actively pushed by edge computing nodes or responds to pull requests from edge computing nodes. Temporary buffering temporarily stores the received raw data packets in the receiver's memory buffer or temporary file to prevent data loss.

[0021] In one optional implementation, the server-side steps include data packet parsing and field extraction steps: Parse the cached raw data packets and extract the target fields, specifically including deserializing the data packets or splitting the fields according to the predefined data format; If the format is JSON, extract the fields using key-value pairs; If it is in binary format, extract it according to the preset byte offset; Verify the validity of each field and remove data packets with incorrect formatting or invalid fields; Perform in-depth validation on the parsed fields to ensure that the data source is trustworthy and has not been tampered with: Authentication is achieved by associating the registration information of edge computing nodes with dynamic pseudo-identity identifiers; Timestamp validity check: Determines whether the timestamp is within a reasonable range; Data integrity verification involves recalculating the checksum in a data packet and comparing it with the checksum carried in the data packet to ensure that the data has not been tampered with during transmission.

[0022] Another aspect of the present invention provides an electronic fence system for protecting user privacy, comprising: User terminal equipment obtains raw location data; The original location data is subjected to localized differential privacy perturbation processing to generate perturbed location data that meets a preset differential privacy budget. The perturbation processing includes optimizing the noise distribution to maintain the accuracy of subsequent electronic fence determination. A dynamic pseudo-identifier is generated for each location report, and the dynamic pseudo-identifier is dynamically bound to the current timestamp and the area identification information of at least one target electronic fence area; A fence determination request carrying the dynamic pseudo-identity identifier, timestamp, area identification information, and disturbance location data is sent to the edge computing node; An edge computing node receives a fence determination request from the user terminal device; It features a lightweight geofence boundary matching algorithm. Based on the disturbance location data in the fence determination request and the area identification information of the corresponding target electronic fence area, the boundary crossing state determination is performed using the geofence boundary matching algorithm. Generate a binarized trigger signal that indicates only entry into or exit from the target electronic fence area; The binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and corresponding area identification information are sent to the server. The server receives a binarized trigger signal, a dynamic pseudo-identity identifier, a timestamp, and the corresponding region identification information from the edge computing node. Based on the binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and area identification information, electronic fence events are recorded, managed, and notified.

[0023] In one optional implementation, when performing localized differential privacy perturbation processing, the user terminal device dynamically adjusts the distribution parameters or differential privacy budget of the added noise based on at least one of the following factors: the accuracy requirements of the target electronic fence area, the distance between the current location of the user terminal device and the boundary of the target electronic fence area, or the user's preset privacy protection level.

[0024] In one optional implementation, the user terminal device generates a dynamic pseudo-identity identifier by: Based on random number generation algorithms, hash algorithms, or a combination of the device's own non-identifiable hardware features with timestamps and area identification information, the dynamic pseudo-identity identifier is generated, so that the dynamic pseudo-identity identifier cannot be associated between events reported in different locations or between different target electronic fence areas, thus tracing back to the user's real identity.

[0025] In one optional implementation, the edge computing node is further configured as follows: Upon receiving a fence determination request, the validity of the dynamic pseudo-identity identifier is verified. After determining the out-of-bounds status of the disturbance location data and generating a binarization trigger signal, the temporarily stored disturbance location data is automatically deleted or anonymized.

[0026] In an optional implementation, a layered encryption and access control mechanism is also included, wherein the communication link between the user terminal device and the edge computing node is encrypted using a first encryption algorithm; The communication link between the edge computing node and the server is encrypted using a second encryption algorithm. The server is also equipped with an attribute-based access control module, which controls the decryption and / or access permissions of data related to electronic fence events based on the attribute information of the access subject and the preset access policy.

[0027] In another aspect, the present invention provides an electronic device comprising: At least one memory stores computer-executable instructions non-transiently; At least one processor, configured to run the computer-executable instructions, The computer-executable instructions are executed by the processor to implement the above-described electronic fence method for protecting user privacy.

[0028] In another aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by at least one processor, implement the above-described electronic fence method for protecting user privacy.

[0029] Effects of the invention: User privacy protection has been significantly enhanced: (1) Existing technologies pose a risk of leakage of user location privacy data. This application uses terminal-side privacy preprocessing to anonymize location data and transmits only the desensitized status signal, effectively preventing privacy leakage.

[0030] (2) In the prior art, fixed user IDs are easily associated with analysis. This application adopts dynamic pseudo-identity mapping to generate temporary anonymous identifiers, blocking attackers from tracking the user's real identity.

[0031] A good balance between privacy protection and functional performance: (1) The existing technology has a contradiction between privacy protection and functional performance. This application solves the conflict between accuracy and privacy by using localized differential privacy perturbation to maintain the accuracy of fence determination while protecting privacy.

[0032] (2) The existing technology is not real-time enough. This application adopts edge computing driven fence determination, completes the boundary judgment nearby, and only sends back the binary trigger signal, which reduces the server load and improves the response speed.

[0033] Improved data security and compliance: (1) Existing technologies do not cover the full lifecycle privacy protection of location data. This application constructs a full lifecycle privacy management system, setting rules and measures for each stage from data collection to destruction, to meet the requirements of data security regulations.

[0034] (2) The existing technology is not compliant enough. This application designs a layered encryption and attribute-based access control policy to achieve fine-grained permission management, prevent unauthorized access, and ensure communication security and data privacy.

[0035] Reduced system operating costs and resource consumption Existing technologies rely on server-side processing, resulting in high server load and high costs. This application reduces server pressure, lowers operating costs and resource consumption, and improves system performance and scalability by distributing computing tasks through edge computing nodes. Attached Figure Description

[0036] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a flowchart provided in Embodiment 1 of the present invention; Figure 2 This is a framework diagram of an electronic fence system for protecting user privacy provided in Embodiment 3 of the present invention; Figure 3 This is a block diagram of the electronic device provided in Embodiment 4 of the present invention; Figure 4 This is a block diagram of a computer-readable storage medium provided in Embodiment 4 of the present invention. Detailed Implementation

[0037] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0038] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0039] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0040] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0041] Example 1: like Figure 1 As shown, this embodiment of the invention provides an electronic fence method for protecting user privacy, comprising the following steps: Step S100: User terminal equipment side steps: Obtain raw location data; perform localized differential privacy perturbation processing on the raw location data to generate perturbed location data that meets a preset differential privacy budget. The perturbation processing includes optimizing the noise distribution to maintain the accuracy of subsequent electronic fence determination; generate a dynamic pseudo-identifier for this location report and dynamically bind the dynamic pseudo-identifier to the current timestamp and the area identification information of at least one target electronic fence area; send a fence determination request carrying the dynamic pseudo-identifier, timestamp, area identification information, and perturbed location data to the edge computing node; Step S200: Edge computing node side steps: Receive a fence determination request from the user terminal device; based on the disturbance location data in the fence determination request and the area identification information of the corresponding target electronic fence area, perform boundary crossing determination using a pre-deployed lightweight geofence boundary matching algorithm; generate a binary trigger signal that only indicates entry or exit from the target electronic fence area; send the binary trigger signal, dynamic pseudo-identity identifier, timestamp, and corresponding area identification information to the server; Step S300: Server-side steps: Receives a binarized trigger signal, a dynamic pseudo-identity identifier, a timestamp, and corresponding area identification information from the edge computing node; records, manages, and notifies electronic fence events based on the binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and area identification information.

[0042] In the above embodiments, the edge-side privacy preprocessing mechanism and localized differential privacy perturbation are implemented: controllable noise is added to the original location data on the terminal device (such as a mobile phone or IoT device) to generate perturbation data that meets differential privacy protection, ensuring that individual user data cannot be reversed and maintaining the accuracy of fence determination through noise distribution optimization. Dynamic pseudo-identity mapping generates a temporary anonymous identifier for each location report and dynamically binds it with a timestamp and fence area ID, blocking the possibility of attackers tracking and associating the user's real identity through long-term data. Edge computing-driven fence determination and lightweight fence logic are implemented: the boundary matching algorithm of the geofence (such as ray-mapping and polygon coverage detection) is deployed to edge computing nodes (such as edge servers on the base station side), and the boundary judgment is completed based on the nearest location data after perturbation. Only a binary trigger signal (such as "enter / leave" status) is sent back to the server to prevent the original location data from leaving the terminal's jurisdiction. Layered encryption and access control, link-level encryption isolation: Layered encryption strategies are adopted for different transmission links from the terminal to the edge node and from the edge node to the server (e.g., lightweight national cryptographic algorithms are used from the terminal to the edge node, and asymmetric encryption algorithms are used from the edge node to the server), reducing computational overhead while ensuring communication security. Attribute-based access control: Fine-grained data decryption permission policies are designed based on user roles and scenario requirements (e.g., only specific guardians are allowed to decrypt fence events in parental control mode) to prevent unauthorized access. Full lifecycle privacy management: Automatic expiration policies for location data are set at the edge node and server (e.g., disturbing location data is deleted immediately after fence event processing), and data flow logs are recorded through blockchain to achieve audit traceability.

[0043] Example 2: like Figure 1As shown, based on Embodiment 1, the steps provided in this embodiment of the invention include generating the dynamic pseudo-identity identifier by: generating the dynamic pseudo-identity identifier based on a random number generation algorithm, a hash algorithm, or a combination of the device's own non-identifiable hardware features and timestamps and regional identification information.

[0044] Specifically, the geofence boundary matching algorithm includes at least one of the ray-based method and the polygon coverage detection algorithm.

[0045] Specifically, the layered encrypted communication steps are as follows: The user terminal device and the edge computing node use a first encryption algorithm to encrypt the transmission of the fence determination request. The edge computing node and the server use a second encryption algorithm to encrypt the transmission of the binarized trigger signal information.

[0046] Specifically, the server-side steps include an access control step. When the server notifies or accesses data about electronic fence events, it adopts an attribute-based access control policy, granting or denying access to the decryption and / or access rights of the access subject to the fence event data based on the access subject's attribute information and the preset access policy.

[0047] Specifically, the server-side steps include data lifecycle management steps: After generating and sending a binarized trigger signal, the edge computing node automatically deletes or anonymizes the temporarily stored disturbance location data. The server sets up automatic expiration and deletion or anonymization processing strategies for stored sensitive information such as dynamic pseudo-identifiers; Blockchain technology is used to record the flow logs of data between user terminal devices, edge computing nodes and servers, enabling audit traceability.

[0048] Specifically, the step of using a pre-deployed lightweight geofence boundary matching algorithm to perform boundary crossing determination includes extracting disturbance location data and the corresponding area identification information of the target electronic fence area from the received fence determination request. Based on the area identification information of the target electronic fence area, the digital boundary description information of the target electronic fence area is obtained from the edge computing node locally or from a preset data source in a secure manner. The digital boundary description information may be a sequence of polygon vertex coordinates, the center coordinates and radius of a circle, or a parameterized representation of other regular / irregular geometric shapes. A lightweight geofence boundary matching algorithm pre-deployed on an edge computing node is invoked and executed to determine the spatial relationship between the disturbed location data and the digital boundary description information of the target geofence area. The lightweight geofence boundary matching algorithm includes, but is not limited to: The ray method involves emitting a ray from the disturbance location in any direction and calculating the number of times the ray intersects with the boundary of the electronic fence polygon. If the number of intersections is odd, the point is determined to be within the region; if it is even, the point is outside the region. The polygon coverage detection algorithm determines whether a point is covered by a polygon by calculating the relative positional relationship between the disturbed location point and each side of the polygon, or by using simple area comparison. A fast judgment algorithm for fences of specific shapes is proposed. For circular fences, the distance between the disturbance location point and the center of the circle is calculated and compared with the radius. For rectangular fences, it is determined whether the horizontal and vertical coordinates of the disturbance location point are within the boundary of the rectangle.

[0049] Specifically, it also includes the steps of obtaining historical states and comparing states: If it is the first determination or historical status is not required, obtain the current disturbance position relative to the target electronic fence area; If it is necessary to determine an entry or exit event, the previous fence status determination result bound to the dynamic pseudo-identity identifier and the target electronic fence area identification information is retrieved from the local temporary storage of the edge computing node. Compare the current state determination result with the previous state determination result; Steps to generate out-of-bounds status determination results: If only the current state is needed, output the internal or external state; If a state comparison is performed: If the current state is internal and the previous state was external, then it is determined that the state has entered an out-of-bounds state. If the current state is external and the previous state was internal, then it is determined that the state has left the outbound state; If the current state is the same as the previous state, it is determined that there is no out-of-bounds error or the state has not changed.

[0050] Specifically, the server-side steps include receiver preparation and communication link establishment steps: The receiving end is pre-configured with communication parameters for the edge computing node, including: Communication protocol adaptation: Enable the corresponding protocol listening service based on the deployment environment of the edge computing node; Security mechanisms are configured, including loading pre-negotiated encryption algorithms, signature verification public keys, or device identity certificates to ensure the confidentiality and integrity of the communication link; Resource reservation allocates network bandwidth, cache space, and process / thread resources for concurrent data reception from edge computing nodes to avoid reception congestion.

[0051] Specifically, the server-side steps for data reception and initial caching are as follows: The receiving end receives data packets sent by the edge computing nodes in real time or periodically through the established communication link, specifically including: Listening and capturing: The protocol listening module captures data packets actively pushed by edge computing nodes or responds to pull requests from edge computing nodes. Temporary buffering temporarily stores the received raw data packets in the receiver's memory buffer or temporary file to prevent data loss.

[0052] Specifically, the server-side steps include data packet parsing and field extraction steps: Parse the cached raw data packets and extract the target fields, specifically including deserializing the data packets or splitting the fields according to the predefined data format; If the format is JSON, extract the fields using key-value pairs; If it is in binary format, extract it according to the preset byte offset; Verify the validity of each field and remove data packets with incorrect formatting or invalid fields; Perform in-depth validation on the parsed fields to ensure that the data source is trustworthy and has not been tampered with: Authentication is achieved by associating the registration information of edge computing nodes with dynamic pseudo-identity identifiers; Timestamp validity check: Determines whether the timestamp is within a reasonable range; Data integrity verification involves recalculating the checksum in a data packet and comparing it with the checksum carried in the data packet to ensure that the data has not been tampered with during transmission.

[0053] In the above embodiments, the data acquisition layer, centered on the terminal device, is responsible for the acquisition and preliminary processing of location data. The terminal device acquires location information in real time through sensors or positioning modules (such as GPS, base station positioning BSP, etc.) and performs data preprocessing operations, including data cleaning (removing outliers), format conversion (unifying the coordinate system), and compression (reducing transmission bandwidth). The preprocessed data provides standardized input for subsequent privacy calculations, ensuring data quality and consistency while reducing the network resource consumption caused by redundant transmission.

[0054] The privacy-preserving computation layer safeguards data security and privacy compliance through two main modules: differential privacy processing and dynamic identity management. Differential privacy processing uses noise injection to perturb the original location data, balancing data availability with the strength of privacy protection. A precision control module dynamically adjusts noise parameters to meet the needs of different scenarios (e.g., lower noise is required for high-precision navigation). Dynamic identity management uses a pseudo-ID generator and collaborative signature technology to assign temporary identifiers (such as hash-based anonymized IDs) to terminal devices, and a time-validation module periodically updates or revokes these IDs to prevent long-term tracking. This layer of technology mitigates the risk of data breaches and supports secure cross-organizational collaboration (e.g., joint analytics).

[0055] The edge decision layer, deployed at network edge nodes (such as base stations and local servers), enables low-latency, localized computation. Its geofencing rule engine, based on preset geofencing policies (such as electronic fence boundaries) and real-time location verification, determines whether a device has crossed boundaries and triggers dynamic threshold management (such as adjusting fence sensitivity). Boundary crossing judgment combines real-time location data with dynamic thresholds to generate boundary crossing signals (such as alarm trigger commands) and transmits the results to the server via a signal generation interface. This layer relies on edge computing resources, reducing server load and improving response speed.

[0056] Cloud Service Layer: This layer is responsible for global management and policy optimization. Alarm Trigger Mechanism: Upon receiving an edge layer boundary violation signal, it matches a preset action (such as SMS notification or device locking) from the response policy library and records event details (time, location, and processing result) through the log persistence module for subsequent auditing. Policy Update Interface: Supports dynamic updates to response policies (such as adding fence rules or adjusting alarm levels), and optimizes algorithm models by combining historical log data (such as machine learning-based anomaly detection). The cloud service layer also provides cross-regional data collaboration capabilities (such as multi-edge node data aggregation and analysis) to support global business scheduling. Terminal-side privacy preprocessing mechanism: Localized differential privacy perturbation.

[0057] Process step design: Multi-fence coordinate mapping: The preset fence area is divided into a grid (side length L), and the user's real position (x,y) is mapped to the nearest grid center (xc,yc); Noise addition: Add Laplace noise to xc and yc to generate the perturbation position (x′, y′); Fence Matching: Determines whether the disturbance location falls within the target fence area, returning only a boolean value (True / False).

[0058] Location coordinate information obfuscation encryption algorithm Algorithm formula: x′=xc+Δx,Δx~Laplace(0, ); y′=yc+Δy,Δy~Laplace(0, ); Dynamic parameter design: Sensitivity L: Grid side length (default 100 meters), which can be dynamically adjusted by the server to control accuracy; Privacy Budget The default value is 0.5. The smaller the value, the greater the noise and the stronger the privacy protection.

[0059] Calculation steps: Set L and according to business needs ; The terminal calls an encrypted random number generator to generate Δx and Δy; Output the perturbation position (x′,y′)(x′,y′).

[0060] Dynamic pseudo-identity mapping; Collaborative signature process: Key generation: The terminal and server pre-share a collaborative signature key pair (SKterm, PKserv); Signature generation: Each time a report is submitted, the terminal generates a temporary private key k and calculates a one-time signature: Sig = Hash(k || Timestamp) mod M (M is the signature space size, default is 106) Verification and destruction: The server discards the key immediately after verifying the signature to prevent replay attacks.

[0061] Lightweight boundary matching algorithm: Input: Perturbation location (x′, y′), list of vertices of the fence polygon {V1, V2, ..., Vn}; Simplified method of ray method: Cast a horizontal ray to the right from (x′, y′) and count the number of intersections with the polygon's edges; Optimization rule: If a ray coincides with a vertex, skip that vertex to reduce computational cost; Output results: If the number of intersections is odd, return "Enter"; If the number of intersections is even, return "Leave".

[0062] Accuracy parameters: Supports setting the maximum error tolerance δ (default 5 meters). If the disturbance location is less than δ from the boundary, a secondary verification is triggered.

[0063] Business rule design:

[0064] Rules Explanation: The terminal and edge node renegotiate the session key every 5 minutes (using the SM4 algorithm) to prevent key leakage.

[0065] Edge nodes only transmit binary status (0 / 1) to the server, without including location coordinates.

[0066] All communication data includes a timestamp (valid for ≤30 seconds) to defend against replay attacks.

[0067] Attribute-based access control: Policy expression: Permit=(Role∈{Guardian,Admin}) / / Role attribute: Guardian or Administrator ∧(Time∈[Tstart,Tend]) / / Time attribute: Specifies the time period ∧(Region==Z) / / Region attribute: Target fence ID ∧(DeviceID∈TrustedList) / / Device attribute: List of trusted devices Execution process: The cloud receives out-of-bounds status requests and extracts the attribute set A={R,T,Z,D}.

[0068] Match the predefined strategy P(A) to generate a temporary decryption key K.

[0069] Edge nodes use K to decrypt associated perturbation data (valid only within the authorized time period).

[0070] Audit log records: Log=(User Identifier (PID), Action Type (Action), Time (Time), Matching Policy (PolicyMatched)).

[0071] Permission granularity: Roles: Guardian, Administrator, Regular User (no decryption permission by default).

[0072] Time: Configure valid time periods by hour / day (e.g., child monitoring: 07:00-19:00).

[0073] Region: Limit fence ID (Z) to prevent cross-region data access.

[0074] Full lifecycle privacy management: Data collection phase: The terminal only collects location data when the user authorizes it (based on a runtime permission model).

[0075] Each data collection is used only for the current fence determination and does not cache historical locations.

[0076] Data processing stage: Edge nodes: Disturbance data is stored for ≤15 minutes after being identified and then automatically deleted (based on a scheduled task).

[0077] Server-side: Only stores out-of-bounds status (no location data), storage period ≤ 30 days (configurable).

[0078] Data destruction phase: Terminal: Clear the raw / perturbation data in memory (SecureZeroMemory) after each report.

[0079] Edge nodes: Record destruction operations via blockchain (blocks contain timestamps, user IDs, and operation types).

[0080] Audit trail: The blockchain records the entire operation chain (process steps: data collection → disturbance → judgment → destruction), and is tamper-proof.

[0081] Data subjects can use their private keys to query their personal data transfer records and exercise their right to delete data.

[0082] Lifecycle rule table:

[0083] Example 3: like Figure 2 As shown, based on Embodiment 2, this embodiment of the invention provides an electronic fence system for protecting user privacy, comprising: User terminal equipment obtains raw location data; The original location data is subjected to localized differential privacy perturbation processing to generate perturbed location data that meets a preset differential privacy budget. The perturbation processing includes optimizing the noise distribution to maintain the accuracy of subsequent electronic fence determination. A dynamic pseudo-identifier is generated for each location report, and the dynamic pseudo-identifier is dynamically bound to the current timestamp and the area identification information of at least one target electronic fence area; A fence determination request carrying the dynamic pseudo-identity identifier, timestamp, area identification information, and disturbance location data is sent to the edge computing node; An edge computing node receives a fence determination request from the user terminal device; It features a lightweight geofence boundary matching algorithm. Based on the disturbance location data in the fence determination request and the area identification information of the corresponding target electronic fence area, the boundary crossing state determination is performed using the geofence boundary matching algorithm. Generate a binarized trigger signal that indicates only entry into or exit from the target electronic fence area; The binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and corresponding area identification information are sent to the server. The server receives a binarized trigger signal, a dynamic pseudo-identity identifier, a timestamp, and the corresponding region identification information from the edge computing node. Based on the binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and area identification information, electronic fence events are recorded, managed, and notified.

[0084] Specifically, when performing localized differential privacy perturbation processing, the user terminal device dynamically adjusts the distribution parameters or differential privacy budget of the added noise based on at least one of the following factors: the accuracy requirements of the target electronic fence area, the distance between the current location of the user terminal device and the boundary of the target electronic fence area, or the user's preset privacy protection level.

[0085] Specifically, the user terminal device generates a dynamic pseudo-identity identifier including: Based on random number generation algorithms, hash algorithms, or a combination of the device's own non-identifiable hardware features with timestamps and area identification information, the dynamic pseudo-identity identifier is generated, so that the dynamic pseudo-identity identifier cannot be associated between events reported in different locations or between different target electronic fence areas, thus tracing back to the user's real identity.

[0086] Specifically, the edge computing node is further configured as follows: Upon receiving a fence determination request, the validity of the dynamic pseudo-identity identifier is verified. After determining the out-of-bounds status of the disturbance location data and generating a binarization trigger signal, the temporarily stored disturbance location data is automatically deleted or anonymized.

[0087] Specifically, it also includes a layered encryption and access control mechanism, wherein the communication link between the user terminal device and the edge computing node is encrypted using a first encryption algorithm; The communication link between the edge computing node and the server is encrypted using a second encryption algorithm. The server is also equipped with an attribute-based access control module, which controls the decryption and / or access permissions of data related to electronic fence events based on the attribute information of the access subject and the preset access policy.

[0088] Example 4 Figure 3 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0089] The electronic device may include a central processing unit / microprocessor / main control chip, etc. 4; and a storage medium 5, coupled to the central processing unit / microprocessor / main control chip, etc. 4, and storing computer-executable instructions therein for performing the steps of various methods of embodiments of the present invention when executed by the processor.

[0090] The central processing unit / microprocessor / main control chip, etc., can include, but are not limited to, one or more processors or microprocessors.

[0091] Storage medium 5 may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (e.g., hard disk, floppy disk, solid-state drive, removable disk, CD-ROM, DVD-ROM, Blu-ray disc, etc.).

[0092] In addition, the electronic device may also include (but is not limited to) a data bus 6, an input / output bus / external bus / device bus 7, a display 8, and input / output devices 9 (e.g., keyboard, mouse, speaker, etc.).

[0093] The central processing unit / microprocessor / main control chip, etc. 4 can communicate with external devices (8, 9, etc.) via I / O bus 7 through wired or wireless network (not shown).

[0094] The storage medium 5 may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip, etc., 4 is running.

[0095] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0096] Figure 4 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0097] like Figure 4As shown, the non-transitory computer-readable storage medium 11 stores instructions, such as computer-readable instructions 10. When the computer-readable instructions 10 are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium 11 can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions 10 stored on the computer-readable storage medium 11, the various methods described above can be performed.

[0098] In the several embodiments provided by this invention, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0099] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0100] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0101] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0102] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for protecting user privacy using an electronic fence, characterized in that, Includes the following steps: User terminal device side steps: Obtain raw location data; perform localized differential privacy perturbation processing on the raw location data to generate perturbed location data that meets a preset differential privacy budget. The perturbation processing includes optimizing the noise distribution to maintain the accuracy of subsequent electronic fence determination; generate a dynamic pseudo-identifier for this location report, and dynamically bind the dynamic pseudo-identifier to the current timestamp and the area identification information of at least one target electronic fence area. A fence determination request carrying the dynamic pseudo-identity identifier, timestamp, area identification information, and disturbance location data is sent to the edge computing node; Edge computing node-side steps: Receive a fence determination request from the user terminal device; Based on the disturbance location data in the fence determination request and the area identification information of the corresponding target electronic fence area, the boundary crossing status determination is performed using a pre-deployed lightweight geofence boundary matching algorithm. Generate a binary trigger signal that indicates entry or exit from the target electronic fence area; send the binary trigger signal, dynamic pseudo-identity identifier, timestamp, and corresponding area identification information to the server; Server-side steps: Receives a binarized trigger signal, a dynamic pseudo-identity identifier, a timestamp, and corresponding area identification information from the edge computing node; records, manages, and notifies electronic fence events based on the binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and area identification information.

2. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The localized differential privacy perturbation processing further includes: dynamically adjusting the distribution parameters or differential privacy budget of the added noise based on at least one of the following factors: the accuracy requirements of the target electronic fence area, the distance between the current location of the user terminal device and the boundary of the target electronic fence area, or the user's preset privacy protection level.

3. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The generation of dynamic pseudo-identifiers includes: generating the dynamic pseudo-identifiers based on random number generation algorithms, hash algorithms, or a combination of the device's own non-identifiable hardware features with timestamps and regional identification information.

4. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The geofence boundary matching algorithm includes at least one of the following: ray casting and polygon coverage detection algorithms.

5. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The edge computing node-side steps include a layered encrypted communication step: The user terminal device and the edge computing node use a first encryption algorithm to encrypt the transmission of the fence determination request. The edge computing node and the server use a second encryption algorithm to encrypt the transmission of the binarized trigger signal information.

6. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The server-side steps include an access control step. When the server notifies or accesses data about electronic fence events, it adopts an attribute-based access control policy, granting or denying access to the decryption and / or access rights of the access subject to the fence event data based on the access subject's attribute information and the preset access policy.

7. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The server-side steps include data lifecycle management steps: After generating and sending a binarized trigger signal, the edge computing node automatically deletes or anonymizes the temporarily stored disturbance location data. The server sets up automatic expiration and deletion or anonymization processing strategies for stored sensitive information such as dynamic pseudo-identifiers; Blockchain technology is used to record the flow logs of data between user terminal devices, edge computing nodes and servers, enabling audit traceability.

8. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The process of using a pre-deployed lightweight geofence boundary matching algorithm to determine the boundary crossing status includes extracting disturbance location data and the corresponding area identification information of the target electronic fence area from the received fence determination request. Based on the area identification information of the target electronic fence area, the digital boundary description information of the target electronic fence area is obtained from the edge computing node locally or from a preset data source in a secure manner. The digital boundary description information may be a sequence of polygon vertex coordinates, the center coordinates and radius of a circle, or a parameterized representation of other regular / irregular geometric shapes. A lightweight geofence boundary matching algorithm pre-deployed on an edge computing node is invoked and executed to determine the spatial relationship between the disturbed location data and the digital boundary description information of the target geofence area. The lightweight geofence boundary matching algorithm includes, but is not limited to: The ray method involves emitting a ray from the disturbance location in any direction and calculating the number of times the ray intersects with the boundary of the electronic fence polygon. If the number of intersections is odd, the point is determined to be within the region; if it is even, the point is outside the region. The polygon coverage detection algorithm determines whether a point is covered by a polygon by calculating the relative positional relationship between the disturbed location point and each side of the polygon, or by using simple area comparison. A fast judgment algorithm for fences of specific shapes is proposed. For circular fences, the distance between the disturbance location point and the center of the circle is calculated and compared with the radius. For rectangular fences, it is determined whether the horizontal and vertical coordinates of the disturbance location point are within the boundary of the rectangle.

9. The electronic fence method for protecting user privacy as described in claim 8, characterized in that, It also includes the steps of obtaining historical states and performing state comparisons: If it is the first determination or historical status is not required, obtain the current disturbance position relative to the target electronic fence area; If it is necessary to determine an entry or exit event, the previous fence status determination result bound to the dynamic pseudo-identity identifier and the target electronic fence area identification information is retrieved from the local temporary storage of the edge computing node. Compare the current state determination result with the previous state determination result; Steps to generate out-of-bounds status determination results: If only the current state is needed, output the internal or external state; If a state comparison is performed: If the current state is internal and the previous state was external, then it is determined that the state has entered an out-of-bounds state. If the current state is external and the previous state was internal, then it is determined that the state has left the outbound state; If the current state is the same as the previous state, it is determined that there is no out-of-bounds error or the state has not changed.

10. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The server-side steps include receiver preparation and communication link establishment steps as follows: The receiving end is pre-configured with communication parameters with the edge computing node, including: Communication protocol adaptation: Enable the corresponding protocol listening service based on the deployment environment of the edge computing node; Security mechanisms are configured to load pre-negotiated encryption algorithms, signature verification public keys, or device identity certificates to ensure the confidentiality and integrity of the communication link; Resource reservation allocates network bandwidth, cache space, and process / thread resources for concurrent data reception from edge computing nodes to avoid reception congestion.

11. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The server-side steps include data reception and initial caching steps as follows: The receiving end receives data packets sent by the edge computing nodes in real time or periodically through the established communication link, specifically including: Listening and capturing: The protocol listening module captures data packets actively pushed by edge computing nodes or responds to pull requests from edge computing nodes. Temporary buffering temporarily stores the received raw data packets in the receiver's memory buffer or temporary file to prevent data loss.

12. The electronic fence method for protecting user privacy as described in claim 1, characterized in that, The server-side steps include data packet parsing and field extraction steps: Parse the cached raw data packets and extract the target fields, specifically including deserializing the data packets or splitting the fields according to the predefined data format; If the format is JSON, extract the fields using key-value pairs; If it is in binary format, extract it according to the preset byte offset; Verify the validity of each field and remove data packets with incorrect formatting or invalid fields; Perform in-depth validation on the parsed fields to ensure that the data source is trustworthy and has not been tampered with: Authentication is achieved by associating the registration information of edge computing nodes with dynamic pseudo-identity identifiers; Timestamp validity check: Determines whether the timestamp is within a reasonable range; Data integrity verification involves recalculating the checksum in a data packet and comparing it with the checksum carried in the data packet to ensure that the data has not been tampered with during transmission.

13. An electronic fence system for protecting user privacy as described in any one of claims 1 to 12, characterized in that, include: User terminal equipment obtains raw location data; The original location data is subjected to localized differential privacy perturbation processing to generate perturbed location data that meets a preset differential privacy budget. The perturbation processing includes optimizing the noise distribution to maintain the accuracy of subsequent electronic fence determination. A dynamic pseudo-identifier is generated for each location report, and the dynamic pseudo-identifier is dynamically bound to the current timestamp and the area identification information of at least one target electronic fence area; A fence determination request carrying the dynamic pseudo-identity identifier, timestamp, area identification information, and disturbance location data is sent to the edge computing node; An edge computing node receives a fence determination request from the user terminal device; It features a lightweight geofence boundary matching algorithm. Based on the disturbance location data in the fence determination request and the area identification information of the corresponding target electronic fence area, the boundary crossing state determination is performed using the geofence boundary matching algorithm. Generate a binarized trigger signal that indicates only entry into or exit from the target electronic fence area; The binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and corresponding area identification information are sent to the server. The server receives a binarized trigger signal, a dynamic pseudo-identity identifier, a timestamp, and the corresponding region identification information from the edge computing node. Based on the binarized trigger signal, dynamic pseudo-identity identifier, timestamp, and area identification information, electronic fence events are recorded, managed, and notified.

14. An electronic fence system for protecting user privacy as described in claim 13, characterized in that, When performing localized differential privacy perturbation processing, the user terminal device dynamically adjusts the distribution parameters of the added noise or the differential privacy budget based on at least one of the following factors: the accuracy requirements of the target electronic fence area, the distance between the current location of the user terminal device and the boundary of the target electronic fence area, or the user's preset privacy protection level.

15. An electronic fence system for protecting user privacy as described in claim 13, characterized in that, The user terminal device generates dynamic pseudo-identity identifiers including: Based on random number generation algorithms, hash algorithms, or a combination of the device's own non-identifiable hardware features with timestamps and area identification information, the dynamic pseudo-identity identifier is generated, so that the dynamic pseudo-identity identifier cannot be associated between events reported in different locations or between different target electronic fence areas, thus tracing back to the user's real identity.

16. An electronic fence system for protecting user privacy as described in claim 13, characterized in that, The edge computing node is further configured as follows: Upon receiving a fence determination request, the validity of the dynamic pseudo-identity identifier is verified. After determining the out-of-bounds status of the disturbance location data and generating a binarization trigger signal, the temporarily stored disturbance location data is automatically deleted or anonymized.

17. An electronic fence system for protecting user privacy as described in claim 13, characterized in that, It also includes a layered encryption and access control mechanism, wherein the communication link between the user terminal device and the edge computing node is encrypted using a first encryption algorithm; The communication link between the edge computing node and the server is encrypted using a second encryption algorithm. The server is also equipped with an attribute-based access control module, which controls the decryption and / or access permissions of data related to electronic fence events based on the attribute information of the access subject and the preset access policy.

18. An electronic device comprising: At least one memory stores computer-executable instructions non-transitory; At least one processor, configured to run the computer-executable instructions, The computer-executable instructions are executed by the processor to provide an electronic fence method for protecting user privacy according to any one of claims 1-12.

19. A computer-readable storage medium, wherein, The computer-readable storage medium stores computer-executable instructions that, when executed by at least one processor, implement an electronic fence method for protecting user privacy according to any one of claims 1-12.

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