User behavior privacy protection method for smart home

Abstract

The invention relates to the technical field of privacy protection, in particular to a user behavior privacy protection method oriented to smart home, which is realized through transmission between a sending end and a receiving end, and specifically comprises the following steps: deploying a sensing link quality module and a dynamic adjustment traffic confusion technology module on a transmission layer in an OSI five-layer model for the sending end; a receiving end deploys a de-obfuscation module on a transmission layer; application data of a sending end is transmitted to a transmission layer, and the link quality sensing module feeds back the current network link quality to the dynamic adjustment flow confusion technology module by monitoring the transmission condition of a TCP (Transmission Control Protocol); the dynamic adjustment flow confusion technology module selects a proper flow confusion technology and confusion intensity according to the feedback, carries out flow confusion on the application data, and then packages the application data into a data packet to be transmitted to a network layer; in the transmission layer of the receiving end, the received data packet is de-obfuscated through the de-obfuscation module, and the original data before the obfuscation is restored, so that the application layer can obtain accurate application data.

Description

Technical Field

[0001] The present invention relates to the technical field of privacy protection, and in particular to a user behavior privacy protection method for smart homes. Background Art

[0002] The application of the Internet of Things is becoming more and more common around the world. With the development of the Internet of Things, the number of smart home devices has increased rapidly. According to research and predictions by relevant institutions, by 2025, 21.3% of households in the world will use smart home devices, and the total number of smart home devices will reach 5.44 billion.

[0003] By connecting to the Internet, smart home devices can achieve remote control of smart terminals, automatic adjustments, and interaction with other devices, providing people with a convenient and intelligent home life experience. However, the network traffic characteristics of these devices are closely related to user behavior and can reflect the user's personal preferences, daily routines, etc. When device traffic is maliciously collected and analyzed, the user's privacy information is at risk of being abused, leaked, or used for commercial gain.

[0004] In recent years, for smart home traffic analysis, existing methods mostly use single obfuscation techniques (such as packet padding, traffic shaping, and false traffic injection) to reduce the recognition rate of user behavior. Although these methods can reduce the recognition rate to below 50%, they usually bring significant communication overhead, especially the increase in transmission delay, which affects the quality of user experience. This delay problem is particularly evident for devices with low delay tolerance, such as cameras, smart doorbells, and voice assistants. Delays may lead to the loss of real-time video surveillance and voice interaction, affecting the effectiveness of security monitoring and user satisfaction with smart devices. Summary of the invention

[0005] The purpose of the present invention is to provide a user behavior privacy protection method for smart homes, aiming to solve the problem that traditional methods bring significant communication overhead and thus affect the quality of user experience, and to achieve a balance between transmission efficiency and user behavior recognition rate.

[0006] To achieve the above object, the present invention provides a user behavior privacy protection method for smart home, comprising the following steps:

[0007] Step 1: The sending end deploys a link quality perception module and a dynamic traffic obfuscation technology module at the transport layer in the five-layer model of Open Systems Interconnection, and the receiving end deploys a deobfuscation module at the transport layer;

[0008] Step 2: The sending end sends application data through the application layer, and the link quality perception module monitors the transmission status of the transmission control protocol and feeds back the current network link quality to the dynamic traffic obfuscation technology module;

[0009] Step 3: The dynamic traffic obfuscation technology module selects the traffic obfuscation technology and obfuscation intensity according to the feedback, performs traffic obfuscation on the application data, and then encapsulates it into a data packet and transmits it to the network layer;

[0010] Step 4: The receiving end obtains the data packet from the network layer, deobfuscates it through the deobfuscation module, and restores the original data before obfuscation.

[0011] Optionally, the execution process of step 2 includes the following steps:

[0012] Step 2.1: The sender sends application data to the transport layer through the application layer;

[0013] Step 2.2: Monitor key parameters of the transport layer TCP connection;

[0014] Step 2.3: Conduct network link quality assessment;

[0015] Step 2.4: Perform anomaly detection to identify network anomalies.

[0016] Optionally, the key parameters of the transport layer transmission control protocol connection in step 2 include the sending window size, slow start threshold and round-trip time, and the perceived link quality module evaluates the network link quality by analyzing the round-trip time, congestion status, delay and bottleneck.

[0017] Optionally, the traffic obfuscation technology in step 3 includes packet filling, packet segmentation, and false traffic injection. Packet filling determines the obfuscation strength by the small / large filling length range, packet segmentation determines the obfuscation strength by simple / complex, and false traffic injection determines the obfuscation strength by low / high injection frequency.

[0018] Optionally, the packet padding mechanism is as follows:

[0019] Let M be the maximum transmission unit MTU, L be the original length of the data packet P, Ф be the number of bytes to be added to the data packet P, and take a random value between 1 and β;

[0020] Ф=random_int(1,β)

[0021] in,

[0022] β=ML

[0023] β is the data packet padding length range, Ф bytes are added to the end of the data packet P to generate an obfuscated data packet P'.

[0024] Optionally, the packet segmentation mechanism is as follows:

[0025] Let Min be the minimum segment size, Smin and Smax be the minimum and maximum values ​​of the segment respectively, and L be the original length of the data packet P;

[0026] When L≥Min, a random segmentation mechanism is implemented, and Ф is the number of bytes to be segmented in the data packet P;

[0027] Ф=random_int(Smin,Smax)

[0028] Smin and Smax are adjustable, and Smax≤MTU.

[0029] Optionally, the fake traffic injection mechanism is as follows:

[0030] Assume that device x is the protection target, F(x) is the traffic of device x, and C(x) is the traffic about device x captured by the attacker;

[0031] Generate fake traffic y based on the communication traffic between device x, network service provider ISP and wireless access point AP, and send y together with F(x) in obfuscation mode;

[0032] After the attacker obtains the traffic in the form of C(x)=F(x)+y, he cannot distinguish the events of device x through the traffic characteristics.

[0033] Optionally, during the deobfuscation process in step 4, for data packet filling, the deobfuscation module removes redundant data by identifying the marker pre-sent by the sender; for data packet segmentation, the deobfuscation module identifies the segmented data packets and reassembles them into the original complete data according to a predetermined order or rule; for false traffic injection, the deobfuscation module identifies and processes false data by identifying the marker pre-sent by the sender.

[0034] The present invention provides a user behavior privacy protection method for smart home, which is implemented through transmission between a sending end and a receiving end, specifically, the sending end deploys a perception link quality module and a dynamic flow obfuscation technology module in the transmission layer of the OSI five-layer model, and the receiving end deploys a deobfuscation module in the transmission layer; the application data of the sending end is transmitted to the transmission layer, and the perception link quality module feeds back the current network link quality to the dynamic flow obfuscation technology module by monitoring the transmission status of TCP; the dynamic flow obfuscation technology module selects appropriate flow obfuscation technology (such as data packet segmentation, flow shaping and false flow injection, etc.) and obfuscation intensity according to the feedback, performs flow obfuscation on the application data, and then encapsulates it into a data packet and transmits it to the network layer; at the transmission layer of the receiving end, the deobfuscation module deobfuscates the received data packet, restores the original data before obfuscation, and enables the application layer to obtain accurate application data. The present invention reduces the user behavior recognition rate to 50% or less without affecting the user experience quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 It is a schematic flow chart of the steps of a method for protecting user behavior privacy in a smart home according to the present invention.

[0037] Figure 2 It is a data flow diagram of a user behavior privacy protection method for smart homes in the OSI model of the present invention.

[0038] Figure 3 It is a schematic diagram of the technical solution of the link quality perception module and the dynamic flow obfuscation adjustment technology module in the method of the present invention.

[0039] Figure 4 It is a schematic diagram of an implementation case of false traffic injection in the method of the present invention.

[0040] Figure 5 It is a schematic diagram of the deobfuscation principle of the deobfuscation module in the method of the present invention. DETAILED DESCRIPTION

[0041] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present invention, and should not be construed as limiting the present invention.

[0042] The following is an explanation of the abbreviations of English terms used in the present invention. If abbreviations are used in the text, please refer to the following explanations:

[0043] Open Systems Interconnection, OSI: Open Systems Interconnection model;

[0044] Transmission Control Protocol, TCP: Transmission Control Protocol;

[0045] Maximum Transmission Unit, MTU: Maximum transmission unit;

[0046] Internet Service Provider, ISP: Internet Service Provider;

[0047] Access Point, AP: wireless access point.

[0048] See also Figure 1 The present invention provides a user behavior privacy protection method for smart home, comprising the following steps:

[0049] S1: The sending end deploys the link quality perception module and the dynamic traffic obfuscation technology module at the transport layer in the five-layer model of Open Systems Interconnection, and the receiving end deploys the deobfuscation module at the transport layer;

[0050] S2: The sending end sends application data through the application layer, and the link quality perception module monitors the transmission status of the transmission control protocol and feeds back the current network link quality to the dynamic traffic obfuscation technology module;

[0051] S3: The dynamic traffic obfuscation technology module selects the traffic obfuscation technology and obfuscation intensity according to the feedback, performs traffic obfuscation on the application data, and then encapsulates it into a data packet and transmits it to the network layer;

[0052] S4: The receiving end obtains the data packet from the network layer, deobfuscates it through the deobfuscation module, and restores the original data before obfuscation.

[0053] Specifically, the present invention senses link quality and uses a variety of obfuscation technologies in a coordinated manner to reduce the user behavior recognition rate to 50% or below without affecting the user experience quality.

[0054] The main modules include link quality perception module, dynamic traffic obfuscation technology module and deobfuscation module, all of which are deployed in the transport layer of the OSI five-layer model. The data flow of the user behavior privacy protection method for smart home in the OSI model is shown as follows: Figure 2 shown.

[0055] First, the application data of the sender is passed to the transport layer. The link quality perception module monitors the transmission status of TCP and feeds back the current network link quality to the dynamic traffic obfuscation technology module.

[0056] Secondly, the traffic obfuscation technology module dynamically adjusts the traffic obfuscation technology (such as packet segmentation, traffic shaping, and false traffic injection, etc.) and obfuscation intensity based on the feedback, performs traffic obfuscation on the application data, and then encapsulates it into data packets and transmits it to the network layer.

[0057] Finally, at the transport layer of the receiving end, the received data packets are deobfuscated by the deobfuscation module to restore the original data before obfuscation, so that the application layer can obtain accurate application data.

[0058] The following is a further explanation based on specific modules:

[0059] like Figure 3 The figure shows the technical solution composition between the link quality perception module and the dynamic traffic obfuscation adjustment technology module.

[0060] 1. Link quality perception module

[0061] The link quality perception module is used to provide timely feedback on the current link communication quality and provide a reference for the subsequent dynamic adjustment of traffic obfuscation technology. This module perceives the link quality based on the TCP congestion control mechanism.

[0062] This module comprehensively evaluates the quality of network links by monitoring key parameters of the transport layer TCP connection (such as the send window size, slow start threshold, round-trip time, etc.). By analyzing the dynamic changes of these parameters, the module can accurately detect the network congestion status, delays and potential bottlenecks, thereby providing strong data support for network optimization and performance tuning. In addition, by monitoring indicators such as the number of retransmissions and packet loss rate of TCP connections, the module can also identify abnormal phenomena in the network and help improve the reliability and stability of data transmission.

[0063] 2. Dynamically adjust the traffic obfuscation technology module

[0064] The traffic obfuscation technology module is dynamically adjusted to select appropriate traffic obfuscation technology and obfuscation strength according to the feedback from the link quality perception module.

[0065] The parameters that need to be adjusted and optimized for this module are as follows:

[0066] 2.1 For data packet padding, the obfuscation strength refers to the size of β (the smaller β is, the smaller the range of data packet padding length is, and the weaker the obfuscation strength is). The padding mechanism is as follows:

[0067] Let M be the maximum transmission unit MTU, L be the original length of the data packet P, Ф be the number of bytes to be added to the data packet P, and take a random value between 1 and β.

[0068] Ф=random_int(1,β)

[0069] in,

[0070] β=ML

[0071] Ф bytes are added to the end of the packet P, generating an obfuscated packet P'.

[0072] 2.2 Regarding packet segmentation, when Min is the same, the obfuscation strength is related to Smin (the smaller Smin is, the more fragmented the packet segmentation is, and the stronger the obfuscation strength is). The segmentation mechanism is as follows:

[0073] Let Min be the minimum segment size, Smin and Smax be the minimum and maximum values ​​of the segment respectively, and L be the original length of the data packet P.

[0074] When L≥Min, a random segmentation mechanism is implemented, and Ф is the number of bytes that the data packet P needs to be segmented.

[0075] Ф=random_int(Smin,Smax)

[0076] Smin and Smax are adjustable, and Smax≤MTU.

[0077] 2.3 Regarding the injection of false traffic, the intensity of confusion is related to the injection frequency (the higher the frequency, the more interference and the stronger the confusion intensity). The mechanism of false traffic injection is as follows:

[0078] Assume that device x is the protection target, F(x) is the traffic of device x, and C(x) is the traffic about device x captured by the attacker;

[0079] Generate fake traffic y based on the communication traffic between device x, network service provider ISP and wireless access point AP, and send y together with F(x) in obfuscation mode;

[0080] After the attacker obtains the traffic in the form of C(x)=F(x)+y, he cannot distinguish the events of device x through the traffic characteristics.

[0081] The following is an explanation through a specific implementation case. Case: When device x performs a certain operation, a fixed traffic pattern will be generated. The attacker generates event-level traffic features of device x through machine learning and other technologies, and infers the operation of device x by capturing the traffic F(x) of device x and matching the traffic features.

[0082] ① When device x performs an operation, the original network traffic is captured, the direction of the data packet is determined, and the constructed false traffic y uses the same destination / source IP, port number, etc. as the device x traffic. The payload is filled using the packet construction tool and dynamically adjusted.

[0083] ② Send y together with F(x), and the attacker gets the traffic of C(x) = F(x) + y, as Figure 4 shown.

[0084] ③ Mix the fake traffic y with F(x), making it difficult for the attacker to successfully match it with the traffic characteristics.

[0085] 3. Deobfuscation module

[0086] The deobfuscation module is used at the receiving end to parse and restore the data packets received at the transport layer, and restore the obfuscated data to its original unobfuscated state. The deobfuscation principle of the deobfuscation module is shown below: Figure 5shown.

[0087] The specific deobfuscation method is as follows:

[0088] ① For data packet padding, the sender inserts specific padding data into the data packet to ensure that the data packet reaches the required size or shape. To distinguish the padding data from the actual data, the sender uses specific identifiers (such as fixed strings or special byte sequences) to mark the padding data. By identifying these identifiers, the receiver can accurately detect and remove the redundant padding data, thereby restoring the original valid data.

[0089] ② Regarding data packet segmentation, the sender will segment a larger data block (exceeding the minimum segment size set by the sender) into multiple smaller data packets when transmitting data. At the receiving end, the deobfuscation module is responsible for identifying these segmented packets and reassembling them into the original complete data according to a predetermined order or rule. The method of identifying these segmented packets includes: using the sequence number in the packet header information to determine the order of the data packets, using the segmentation identifier to identify which data packets belong to the same segmentation process, using the checksum to verify the integrity of the data packet, and indicating the end point of the data transmission through the end marker. The combination of these technologies enables the receiving end to effectively identify and reassemble the segmented data packets, ensuring the integrity and correctness of data transmission.

[0090] ③ For false traffic injection, at the sending end, false traffic injection is to confuse traffic by inserting forged data packets between real data packets. The deobfuscation module at the receiving end needs to identify and filter out these forged false traffic. The false data packets constructed by the sending end will carry specific tags or formats to distinguish them from normal data packets. The deobfuscation module identifies false traffic by detecting these specific tags, and discards or ignores them, and only retains and processes valid real data packets. In this way, the receiving end can ensure that the received data is accurate and credible.

[0091] To sum up, compared with the traditional method using a single obfuscation technology, the present invention dynamically adjusts the traffic obfuscation technology and the obfuscation strength according to the user's network condition. When the user's network condition is good, an obfuscation technology with a larger overhead and a stronger obfuscation strength can be selected. When the user's network condition is poor, an obfuscation technology with a smaller overhead and a weaker obfuscation strength can be selected. The obfuscation scheme can be adjusted more flexibly, which is more conducive to achieving the goal of reducing the user behavior recognition rate within the acceptable range for the user.

[0092] Compared with the prior art, the present invention has the following beneficial effects:

[0093] (1) Ensuring user experience quality: The link quality perception module dynamically monitors the network status to ensure that the user experience is not affected when selecting and adjusting the obfuscation technology, especially for smart home devices with low latency tolerance.

[0094] (2) Flexible adaptation to network conditions: Dynamically adjust the traffic obfuscation technology module based on real-time network link quality feedback, select the most appropriate obfuscation technology and strength, and optimize communication efficiency.

[0095] (3) Easy deployment: The present invention is deployed and implemented only in the transport layer of the OSI five-layer model, and has good versatility and operability.

[0096] What is disclosed above is only a preferred embodiment of the present invention, and it certainly cannot be used to limit the scope of rights of the present invention. Ordinary technicians in this field can understand that all or part of the processes of the above embodiment and equivalent changes made according to the claims of the present invention still fall within the scope of the invention.

Claims

1. A user behavior privacy protection method for smart home, characterized in that: The following steps are involved: Step 1: The sending end deploys a link quality perception module and a dynamic traffic obfuscation technology module at the transport layer in the five-layer model of Open Systems Interconnection, and the receiving end deploys a deobfuscation module at the transport layer; Step 2: The sending end sends application data through the application layer, and the link quality perception module monitors the transmission status of the transmission control protocol and feeds back the current network link quality to the dynamic traffic obfuscation technology module; Step 3: The dynamic traffic obfuscation technology module selects the traffic obfuscation technology and obfuscation intensity according to the feedback, performs traffic obfuscation on the application data, and then encapsulates it into a data packet and transmits it to the network layer; Step 4: The receiving end obtains the data packet from the network layer, deobfuscates it through the deobfuscation module, and restores the original data before obfuscation.

2. The user behavior privacy protection method for smart home according to claim 1, characterized in that: The execution process of step 2 includes the following steps: Step 2.1: The sender sends application data to the transport layer through the application layer; Step 2.2: Monitor key parameters of the transport layer TCP connection; Step 2.3: Conduct network link quality assessment; Step 2.4: Perform anomaly detection to identify network anomalies.

3. The user behavior privacy protection method for smart home as claimed in claim 2, characterized in that: The key parameters of the transport layer transmission control protocol connection in step 2 include the sending window size, slow start threshold and round-trip time. The link quality perception module evaluates the network link quality by analyzing the round-trip time, congestion status, delay and bottleneck.

4. The user behavior privacy protection method for smart home as claimed in claim 3, characterized in that: The traffic obfuscation techniques in step 3 include packet filling, packet segmentation, and false traffic injection. Packet filling determines the obfuscation strength by the small / large filling length range, packet segmentation determines the obfuscation strength by simple / complex, and false traffic injection determines the obfuscation strength by low / high injection frequency.

5. The user behavior privacy protection method for smart home as claimed in claim 4, characterized in that: The padding mechanism for packet padding is as follows: Let M be the maximum transmission unit MTU, L be the original length of the data packet P, Ф be the number of bytes to be added to the data packet P, and take a random value between 1 and β; Ф=random_int(1,β) in, β=ML β is the data packet padding length range, Ф bytes are added to the end of the data packet P to generate an obfuscated data packet P'.

6. The user behavior privacy protection method for smart home according to claim 5, characterized in that: The segmentation mechanism of packet segmentation is as follows: Let Min be the minimum segment size, Smin and Smax be the minimum and maximum values ​​of the segment respectively, and L be the original length of the data packet P; When L≥Min, a random segmentation mechanism is implemented, and Ф is the number of bytes to be segmented in the data packet P; Ф=random_int(Smin,Smax) Smin and Smax are adjustable, and Smax≤MTU.

7. The user behavior privacy protection method for smart home according to claim 6, characterized in that: The fake traffic injection mechanism is as follows: Assume that device x is the protection target, F(x) is the traffic of device x, and C(x) is the traffic about device x captured by the attacker; Generate fake traffic y based on the communication traffic between device x, network service provider and wireless access point, and send y together with F(x) in obfuscation mode; After the attacker obtains the traffic in the form of C(x)=F(x)+y, he cannot distinguish the events of device x through the traffic characteristics.

8. The user behavior privacy protection method for smart home according to claim 7, characterized in that: In the deobfuscation process of step 4, for data packet filling, the deobfuscation module removes redundant data by identifying the marker pre-sent by the sender; for data packet segmentation, the deobfuscation module identifies the segmented data packets and reassembles them into the original complete data according to a predetermined order or rule; for false traffic injection, the deobfuscation module identifies and processes false data by identifying the marker pre-sent by the sender.

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