Method and system for lightweight conditional anonymous security communication on device side for medical internet of things

Abstract

The application discloses a medical internet of things oriented device side lightweight conditional anonymous security communication method and system, and belongs to the technical field of information security. The method comprises the following steps: a trusted center initializes system parameters; a medical internet of things device registers to generate a temporary public-private key pair, calculates a device anonymous identity, an authentication factor, a signature auxiliary parameter and a signature key; after the device collects medical data, the device generates a conditional anonymous signature by using pre-computed parameters; a server restores a temporary public key by algebraic operation and verifies the digest of the temporary public key, and determines whether to accept the data; in an abnormal case, the trusted center restores the real identity of the device and traces the device. The application realizes space-time separation of pre-computation in a registration stage and lightweight combined operation in a real-time stage, compresses device side signature operation to a hash and a finite field multiplication level, realizes no pair operation restoration and self-verification through algebraic closed loop design of device authentication factor embedded signature coefficient, and achieves privacy protection and safety supervision unification through homologous binding of an anonymous identity and a signature parameter.

Description

Technical Field

[0001] This invention relates to the field of information security technology, and in particular to a lightweight conditional anonymity secure communication method and system for the device side of the Internet of Things in healthcare, which is applicable to secure data transmission, identity authentication and privacy protection between resource-constrained medical terminal devices and medical servers. Background Technology

[0002] The Internet of Medical Things (IoMT) is a crucial infrastructure for smart healthcare systems. It connects various heterogeneous devices, including human physiological sensors, wearable health monitoring terminals, bedside monitoring devices, mobile nursing terminals, in-hospital medical instruments, and edge computing nodes, to a wireless network to continuously collect, transmit, and intelligently analyze patients' vital signs. In typical applications, medical data includes electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, blood oxygen saturation, blood pressure, blood glucose, body temperature, and medication and behavioral records. This data is typically generated at high frequency, continuously, or through event-triggered mechanisms and uploaded to hospital information systems, regional healthcare platforms, or cloud-based health management platforms via Bluetooth, WiFi, 5G, or dedicated medical wireless networks. This data supports critical functions such as remote diagnosis and treatment, chronic disease management, emergency alerts, and smart hospital dispatching.

[0003] As the scale of medical IoT applications continues to expand, its network environment is gradually exhibiting characteristics such as a huge number of devices, diverse node types, complex communication environments, and blurred security boundaries. On the one hand, a large number of wearable devices and home medical terminals are in open network environments for extended periods, making them vulnerable to external attacks; on the other hand, medical data itself is highly sensitive and has strong privacy attributes. Once leaked, it will directly involve patient identity information, health status, and even disease risk assessment results. Therefore, it places far higher demands on communication security than ordinary IoT systems.

[0004] In existing technologies, various cryptographic methods are typically employed for authentication and data protection to ensure secure communication in the medical IoT field. Common solutions include digital signature methods based on the RSA public-key cryptosystem, authentication and key negotiation methods based on elliptic curve cryptography, identity-based encryption, and group signature or anonymous authentication methods based on bilinear pairings. Furthermore, to reduce computational overhead, some studies employ symmetric encryption combined with lightweight hash functions to construct medical data protection methods, or introduce hybrid encryption models to achieve secure end-to-end data transmission. Simultaneously, some research introduces trusted third-party institutions into the system for unified key management and authentication, thereby improving overall system security. However, the aforementioned existing technologies still have several shortcomings in practical medical IoT deployments, specifically as follows: First, regarding computational complexity and device adaptability, many existing public-key cryptography-based authentication methods rely on complex mathematical operations, such as elliptic curve scalar multiplication, modular exponentiation, and bilinear pairing. While these operations can be executed efficiently on cloud servers or high-performance gateways, resource-constrained medical terminal devices, such as wearable ECG monitoring patches, portable blood glucose meters, smart infusion pumps, and home health monitoring nodes, have limited CPU performance, small memory capacity, and limited battery capacity, making it difficult to continuously execute high-frequency cryptographic operations. In continuous monitoring scenarios, this computational burden leads to increased device response latency and faster power consumption, thus affecting the continuity and real-time nature of medical data acquisition, and may even cause information delays or loss in critical medical early warning scenarios.

[0005] Secondly, regarding communication efficiency, existing security authentication methods typically employ multi-stage interaction, including device registration, identity authentication, session key negotiation, and data transmission. While these multi-round communication methods exhibit acceptable performance in traditional internet environments with high network stability, in the medical IoT environment, due to fluctuations in wireless channel quality, high device mobility, and frequent dynamic changes in network topology, multi-round interactions easily introduce additional latency and increase the probability of communication failure. Furthermore, under conditions of large-scale concurrent device access, multi-round authentication methods can also cause network congestion, reducing overall system throughput.

[0006] Secondly, regarding security oversight and accountability, existing anonymous communication solutions often emphasize user privacy protection but lack a unified method for tracing and accountability. Once malicious behavior occurs in the system, such as falsifying medical monitoring data, altering vital sign records, illegally accessing medical networks, or sending false emergency alerts, the system typically struggles to promptly locate the source of the attack and effectively identify and audit the responsible party. While this "strong anonymity, weak oversight" design model protects user privacy to some extent, it poses serious security governance risks in the highly sensitive field of healthcare.

[0007] Furthermore, regarding system deployment and engineering adaptability, some studies have attempted to introduce complex security methods, such as blockchain-assisted authentication, multi-party secure computation, and attribute-based cryptographic access control, to enhance system security and trustworthiness. However, these methods typically place high demands on computing resources, storage resources, and network bandwidth, making them difficult to deploy directly in large-scale, low-power medical IoT devices. At the same time, these solutions often increase system architecture complexity and maintenance costs, hindering rapid promotion and implementation in medical institutions.

[0008] In summary, existing secure communication technologies for the medical IoT suffer from significant shortcomings in terms of computational efficiency, communication overhead, privacy protection capabilities, and traceability and regulatory methods. These limitations make it difficult to simultaneously meet the comprehensive requirements of lightweight device deployment, real-time data transmission, and compliant security supervision in the medical IoT. Therefore, there is an urgent need to propose a lightweight conditional anonymity secure communication method for the medical IoT device side to achieve a unified secure communication goal of low computational overhead, high privacy protection, and conditional traceability. Summary of the Invention

[0009] Based on the above analysis, the present invention aims to provide a lightweight conditional anonymity secure communication method and system for the device side of the Internet of Things (IoT) in the medical field; it solves the problems of limited computing resources of medical IoT terminals, leakage of medical data privacy, and lack of supervision and accountability for anonymous communication.

[0010] This invention discloses a lightweight conditional anonymity secure communication method for the device side in the medical Internet of Things (IoT), comprising: Device registration steps: Medical IoT devices (MIDs) register with the Trusted Center using their unique identifier, generate a temporary public-private key pair, and calculate the device's anonymous identity, device authentication factor, signature auxiliary parameters, and device signature key. The device authentication factor is a cryptographic digest of the temporary public key, the signature auxiliary parameters are the product of the temporary private key and the inverse of the device authentication factor, and the device signature key contains the sum of relevant terms from the temporary private key and the system private key. Furthermore, the device authentication factor participates in both the generation of the signature auxiliary parameters and the verification of the signature. Medical data signing steps: After the medical IoT device (MID) collects medical data, it generates a conditional anonymous signature using signature auxiliary parameters, device signature key, medical data, device anonymous identity, temporary public key, and time parameters, and then sends it. Signature verification steps: The medical server performs algebraic operations based on the conditional anonymous signature, system public key, device authentication factor, data binding value, and random challenge value to directly recover the temporary public key and verify whether the cryptographic digest of the temporary public key is equal to the device authentication factor to determine whether to accept the medical data.

[0011] Furthermore, the device registration step includes: 1) Medical IoT devices (MIDs) are registered using their unique identifier (ID) and a temporary private key is randomly selected. Calculate the temporary public key The temporary public key This serves as a randomly bound parameter in subsequent anonymous signatures; where... Choosing an elliptic curve group for the center of trust generator, for group hierarchy, Modulus The multiplication group; 2) Medical IoT device MID calculation includes device anonymity, device authentication factor, signature auxiliary parameters, and device signature key; among which, Device Anonymity ;in, This represents the XOR operation. The system public key; A hash function defined for a trust center; Equipment certification factors ; A hash function defined for a trust center; Signature auxiliary parameters ; Device signing key ,in, This is the system master key; ; 3) Medical IoT device MID storage For use in subsequent medical data signing.

[0012] Furthermore, the medical data signing step includes: 1) Medical IoT device MID generates current timestamp Used to resist replay attacks; 2) Medical IoT devices (MIDs) will integrate medical data. Device Anonymity Temporary public key and current timestamp Perform multi-way hashing to obtain the data binding value and the random challenge value; Data binding hash value ; Random challenge hash value ; in, , The hash function defined for the center of trust Data collected for medical IoT devices (MID); Used to bind medical data content, Used to generate random challenges; 3) The Medical Internet of Things (MID) device uses a random challenge value to scale the sum of the products of the signature auxiliary parameters, the data binding value, and the device signature key to obtain the signature value. ; 4) Medical IoT device MID generation includes a signature value. Temporary public key and current timestamp Device-side conditional anonymous signature ; 5) Medical IoT device MID transmission To the medical server MS.

[0013] Furthermore, the data collected by the medical IoT device (MID) includes health monitoring data such as electrocardiogram (ECG) data, blood glucose data, blood pressure data, and body temperature data.

[0014] Furthermore, the signature verification step includes: 1) Verify timestamp Does it meet the valid time window requirement? If the condition is not met, then the data should be rejected; where, For the current moment, The effective time window width; 2) Perform independent hash reconstruction based on public parameters; the reconstructed hash value includes: ; ; ; 3) By performing a combination operation on the elliptic curve group using the signature value, system public key, device authentication factor, and reconstructed hash value, the temporary public key can be directly obtained. ; , 4) Verify whether the cryptographic digest of the recovered temporary public key is equal to the device authentication factor; that is, determine... If the data is valid, the source of the medical data is confirmed to be legal, the device identity is valid, and the data has not been tampered with; otherwise, the data is rejected.

[0015] Furthermore, a system initialization step is included before the device registration step: 1) Trusted Center (TA) selects elliptic curve group Generator with group level Determine the mathematical foundation of cryptographic operations; 2) The Trusted Center (TA) randomly selects the system master key. Public key of computing system Establish an asymmetric root of trust; among which, Modulus The multiplication group; 3) The Trusted Center (TA) defines multiple sets of hash functions for different output domains, which respectively support anonymous identity, parameter binding, data verification, and challenge generation; Specifically, this includes hash functions. , , , ;in, , ;in, , Indicates the actual identity of medical IoT devices; 4) Trusted Center (TA) publishes global parameters For use by system entities, the master key s is secretly stored for device key generation and anomaly tracing.

[0016] Furthermore, the system also performs anomaly device identification tracking steps: Under normal circumstances, medical IoT devices (MIDs) only operate under an anonymous device identity. During communication, the medical server cannot obtain the patient's true identity. When malicious upload of false medical data, counterfeiting of medical IoT devices, unauthorized access to the medical platform, or abnormal behavior attacks occur, the medical server submits a tracking request to the Trusted Center (TA). The TA then restores the device's true identity and performs device revocation, security audit, or accountability tracking, achieving a balance between privacy protection and security supervision.

[0017] Furthermore, the abnormal device identification tracking steps include: 1) Trusted Center (TA) obtains the anonymous identity of the device. and the temporary public key in the signature ; 2) The Trusted Center (TA) utilizes the system master key. For temporary public keys Perform scalar multiplication ; Restore temporary private key With system public key The result of the calculation; 3) The Trusted Center (TA) will anonymize the device's identity. and Perform an XOR operation on the hash value to determine the device's true identity. ; 4) The Trusted Center (TA) performs device removal, security audit, or accountability tracking based on the recovery results.

[0018] Another aspect of the present invention discloses a device-side lightweight conditional anonymity secure communication system for the medical Internet of Things (IoT), which executes the device-side lightweight conditional anonymity secure communication method for the medical IoT as described above. The system includes: Trusted Center (TA) is used to perform system initialization, device registration management, and abnormal device identification tracking. Medical Internet of Things (MID) devices are used to perform device registration, medical data signature generation, and transmission. The medical server (MS) is used to receive medical data and conditional anonymous signatures, and to perform signature verification to determine whether to accept the medical data.

[0019] Furthermore, the medical IoT devices include wearable electrocardiogram monitoring devices, portable blood glucose meters, smart infusion pumps, ambulatory blood pressure monitors, or bedside monitoring devices.

[0020] Compared with existing secure communication methods for the Internet of Things (IoT) in the medical field, the lightweight conditional anonymity secure communication method and system for the device side in the medical IoT disclosed in this invention effectively solves the problems of limited computing resources of medical IoT terminals, leakage of medical data privacy, and lack of supervision and accountability for anonymous communication by designing a lightweight anonymity signature mechanism, a conditional anonymity identity protection mechanism, and a trusted tracking mechanism on the device side. It has the following beneficial effects: 1) Reduce computing overhead on the medical IoT device side and improve adaptability to resource-constrained terminals. This invention addresses the limited computing power of medical terminals such as wearable medical devices, portable vital sign acquisition devices, and bedside monitoring terminals by designing a lightweight conditional anonymization signature method on the device side. During medical data upload, the device does not need to perform complex bilinear pairing operations, large-scale exponential operations, or multi-round authentication interactions; it only needs to perform a small number of elliptic curve scalar operations, hash calculations, and simple finite field operations to generate a secure signature. Compared to traditional authentication schemes that rely on highly complex cryptographic operations, this invention concentrates the main computational process in the device registration stage, while employing a lightweight signature generation method during real-time data transmission, thereby significantly reducing the device's CPU computational burden and energy consumption. Since medical monitoring devices typically need to operate continuously for extended periods, such as continuously collecting physiological parameters like ECG, blood oxygen, and blood pressure, this invention reduces the computational resource consumption during a single data upload, enabling the device to maintain stable operation under low power consumption conditions and improving the real-time monitoring and continuous service capabilities of the medical IoT system.

[0021] 2) Implement medical data source authentication to ensure data integrity and authenticity. This invention binds medical data content, device anonymity, and timestamps to the signature calculation process, creating a strong correlation between the generated conditionally anonymous signature and the specific medical data. Upon receiving data, the medical server verifies the signature relationship to determine if the data originates from a legitimate medical device. Since attackers cannot generate a valid signature without knowing the device's secret parameters, this effectively defends against medical data forgery attacks, data tampering attacks, and unauthorized device access attacks. Furthermore, the introduction of a timestamp mechanism prevents the reuse of historical signature data, effectively preventing replay attacks and improving the integrity and reliability of medical data transmission.

[0022] 3) Protect patient privacy and achieve a balance between anonymous communication and security monitoring. Given the highly sensitive nature of medical data, this invention employs a conditional anonymity mechanism. During normal communication, medical devices do not directly expose their real identity ID. Instead, they generate an anonymous identity identifier (PID) using random parameters and cryptographically hide the device's true identity information. During normal medical services, the medical server can only verify the device's legitimacy and cannot directly obtain the real identity information corresponding to the patient or device, thus reducing the risk of patient identity exposure due to device identifier leakage. Simultaneously, when malicious behavior occurs in the system, such as falsifying medical data, illegally accessing the medical platform, or sending abnormal health information, the Authorized Trust Center (TA) can use the system master key to perform identity tracking, recovering the corresponding real identity ID based on the anonymous PID, achieving responsibility location, security auditing, and abnormal device management. Therefore, this invention protects patient privacy while meeting the security supervision requirements of the medical system. Attached Figure Description

[0023] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts. Figure 1 This is a core flowchart of the device-side lightweight conditional anonymity secure communication method in this embodiment of the invention; Figure 2 This is a complete flowchart of the device-side lightweight conditional anonymity secure communication method in an embodiment of the present invention; Figure 3 This is a schematic diagram of the device-side lightweight conditional anonymity secure communication system in an embodiment of the present invention. Detailed Implementation

[0024] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and, together with the embodiments of the present invention, serve to illustrate the principles of the present invention.

[0025] Example 1 One embodiment of the present invention discloses a lightweight conditional anonymity secure communication method for the device side in the medical Internet of Things, the core process of which is as follows: Figure 1 As shown, it includes: Device registration steps: Medical IoT devices (MIDs) register with the Trusted Center using their unique identifier, generate a temporary public-private key pair, and calculate the device's anonymous identity, device authentication factor, signature auxiliary parameters, and device signature key. The device authentication factor is a cryptographic digest of the temporary public key, the signature auxiliary parameters are the product of the temporary private key and the inverse of the device authentication factor, and the device signature key contains the sum of relevant terms from the temporary private key and the system private key. Furthermore, the device authentication factor participates in both the generation of the signature auxiliary parameters and the verification of the signature. Medical data signing steps: After the medical IoT device (MID) collects medical data, it generates a conditional anonymous signature using signature auxiliary parameters, device signature key, medical data, device anonymous identity, temporary public key, and time parameters, and then sends it. Signature verification steps: The medical server performs algebraic operations based on the conditional anonymous signature, system public key, device authentication factor, data binding value, and random challenge value to directly recover the temporary public key and verify whether the cryptographic digest of the temporary public key is equal to the device authentication factor to determine whether to accept the medical data.

[0026] Preferably, a system initialization step is included before the device registration step; this stage is performed by the Trusted Center (TA) and is used to establish unified security parameters and a root of trust for the medical IoT system, providing a cryptographic foundation for device registration, anonymous signatures, and conditional traceability. The specific steps are as follows: 1) Trusted Center (TA) selects elliptic curve group Generator with group level Determine the mathematical foundation of cryptographic operations; 2) The Trusted Center (TA) randomly selects the system master key. Public key of computing system Establish an asymmetric root of trust; among which, Modulus The multiplication group; 3) The Trusted Center (TA) defines multiple sets of hash functions for different output domains, which respectively support anonymous identity, parameter binding, data verification, and challenge generation; Specifically, this includes hash functions. , , , ;in, , ;in, , Indicates the actual identity of medical IoT devices; 4) Trusted Center (TA) publishes global parameters For use by system entities, the master key s is secretly stored for device key generation and anomaly tracing.

[0027] This step centralizes the system's root of trust in a single trusted center (TA). By leveraging the discrete logarithmic difficulty of elliptic curve groups, it ensures the irreversible derivation between the system's public key and master key. Hash functions with different output domains are used to achieve identity anonymization, parameter binding, data integrity verification, and random challenge generation, forming a layered cryptographic toolset. This provides a unified security parameter foundation for device registration, anonymous signatures, and conditional traceability in the open network environment of the medical IoT, achieving a balance between system manageability and decentralized verification.

[0028] Furthermore, the device registration step is used to generate lightweight anonymous authentication credentials for resource-constrained medical IoT devices, moving complex calculations to the registration stage so that the real-time signature stage only requires lightweight combinational calculations. The specific steps are as follows: 1) Medical IoT devices (MIDs) are registered using their unique identifier (ID) and a temporary private key is randomly selected. Calculate the temporary public key The temporary public key This serves as a randomly bound parameter in subsequent anonymous signatures; where... Choosing an elliptic curve group for the center of trust generator, for group hierarchy, Modulus The multiplication group; 2) Medical IoT device MID calculation includes device anonymity, device authentication factor, signature auxiliary parameters, and device signature key; among which, Device Anonymity ;in, This represents the XOR operation. The system public key; A hash function defined for a trust center; Equipment certification factors ; A hash function defined for a trust center; Signature auxiliary parameters ; Device signing key ,in, This is the system master key; ; 3) Medical IoT device MID storage For use in subsequent medical data signing.

[0029] This step uses the same temporary private key. Using this as a seed, a coupled parameter structure is generated: the device's anonymous identity hides its true identity through an XOR operation; the device authentication factor, as a cryptographic digest of the temporary public key, becomes the verification anchor; the signature auxiliary parameter embeds the device authentication factor into the signature coefficient in the form of an inverse; and the device signature key adds the temporary private key to the relevant items of the system private key and binds them to system trust. This allows the device authentication factor to participate in both the generation of the signature auxiliary parameter and the verification of the signature, forming an algebraic closed loop of "signature generation - verification recovery - identity verification," thereby achieving a balance of low computational overhead, high privacy protection, and conditional traceability on resource-constrained medical IoT terminals.

[0030] Furthermore, the medical data signing step is used to generate a conditionally anonymous signature strongly bound to the medical data on the device side with extremely low computational overhead, achieving verifiable data source, anonymized identity, and tamper-proof content. The specific steps are as follows: 1) Medical IoT device MID generates current timestamp Used to resist replay attacks; 2) Medical IoT devices (MIDs) will integrate medical data. Device Anonymity Temporary public key and current timestamp Multi-way hashing is performed to obtain a data binding value and a random challenge value. The data binding value is used to strongly bind medical data content with the device's anonymous identity, temporary public key, and timestamp, while the random challenge value is used to introduce unpredictability. Data binding hash value ; Random challenge hash value ; in, , The hash function defined for the center of trust Data collected for medical IoT devices (MID); Used to bind medical data content, Used to generate random challenges; Specifically, the data collected by the medical IoT device MID includes health monitoring data such as electrocardiogram data, blood glucose data, blood pressure data, and body temperature data; 3) The Medical Internet of Things (MID) device uses a random challenge value to scale the sum of the products of the signature auxiliary parameters, the data binding value, and the device signature key to obtain the signature value. ; Among them, the inverse structure of the device authentication factor in the signature auxiliary parameters and the temporary private key in the device signature key form a specific algebraic combination containing the temporary public key and the system public key in the scaling operation, so that the verification end can directly recover the temporary public key and verify its digest through public parameters and algebraic operations. 4) Medical IoT device MID generation includes a signature value. Temporary public key and current timestamp Device-side conditional anonymous signature ; 5) Medical IoT device MID transmission To the medical server MS.

[0031] In this step, the data binding value Medical data Content and device anonymity Temporary public key and timestamp Strong binding, random challenge value Introducing unpredictability, signature auxiliary parameters The device authentication factor inverse structure and the temporary private key in the device signature key form a specific algebraic combination containing a temporary public key and a system public key during scaling operations. This allows the verification end to directly recover the temporary public key and verify its digest through public parameters and algebraic operations. This compresses the real-time signature operation on the device side to a lightweight level involving only hash operations, finite field multiplication, and scalar multiplication, eliminating the need for bilinear pairing operations and modular exponentiation operations. While ensuring the authentication of medical data sources, data integrity, and authenticity, this significantly reduces the computational burden and energy consumption of resource-constrained medical terminals, enabling real-time data acquisition and low-latency secure transmission in high-frequency continuous monitoring scenarios.

[0032] Furthermore, the signature verification step receives and verifies the legality of medical data and conditionally anonymous signatures, confirming the reliability of the data source and the absence of data tampering without obtaining the true identity of the device, and defending against replay attacks. The specific steps are as follows: 1) Verify timestamp Does it meet the valid time window requirement? If the condition is not met, then the data should be rejected; where, For the current moment, Defines the effective time window width; defends against replay attacks by rejecting expired or premature data. 2) Perform independent hash reconstruction based on publicly available parameters; ensure that the verification end does not need to rely on device-side pre-computation; The reconstructed hash values ​​include: ; ; ; 3) Use the signature value, system public key, device authentication factor and reconstructed hash value to perform combination operations on the elliptic curve group to directly solve for the temporary public key; , 4) Verify whether the cryptographic digest of the recovered temporary public key is equal to the device authentication factor; that is, determine... If the data is valid, the source of the medical data is confirmed to be legal, the device identity is valid, and the data has not been tampered with; otherwise, the data is rejected.

[0033] This step compresses the two-step process of "verifying the signature first and then checking the identity" in traditional signature verification into an integrated operation of "algebraic recovery + self-verification". By utilizing the inverse structure of the device authentication factor embedded in the signature auxiliary parameters, the recovered temporary public key naturally satisfies the verification equation. There is no need to query certificates or perform complex pairing operations. This reduces the server-side verification operation to a lightweight level involving only hash operations, finite field operations and elliptic curve scalar multiplication. While confirming the legality of the medical data source, the validity of the device identity, and the fact that the data has not been tampered with, it significantly reduces the server computing burden in large-scale concurrent scenarios and improves the overall throughput and real-time response performance of the medical IoT system.

[0034] In the preferred embodiment, the system also performs an abnormal device identification tracking step: Under normal circumstances, the medical IoT device MID only uses the device's anonymous identity. During communication, the medical server cannot obtain the patient's true identity. However, in cases of malicious uploading of false medical data, counterfeiting of medical IoT devices, unauthorized access to the medical platform, or other abnormal behavior attacks, the medical server submits a tracking request to the Trusted Data Center (TA). The TA then restores the device's true identity and performs device revocation, security auditing, or accountability tracking, achieving a balance between privacy protection and security oversight. The specific steps are as follows: 1) Trusted Center (TA) obtains the anonymous identity of the device. and the temporary public key in the signature ; 2) The Trusted Center (TA) utilizes the system master key. For temporary public keys Perform scalar multiplication ; Restore temporary private key With system public key The result of the calculation; 3) The Trusted Center (TA) will anonymize the device's identity. and Perform an XOR operation on the hash value to determine the device's true identity. ; 4) The Trusted Center (TA) performs device removal, security audit, or accountability tracking based on the recovery results.

[0035] This step utilizes the mapping between the system public key and the system master key during the generation and tracking of the device's anonymous identity. This enables only the authorized trusted center, which possesses the system master key, to have traceability capabilities. No other entity, including the medical server, can deduce the real identity from the device's anonymous identity or communication content. Thus, while ensuring that patient privacy is not exposed during normal medical services, it achieves controllable supervision and accountability for abnormal behavior, achieving a balance between high privacy protection and compliant security supervision in the medical IoT.

[0036] like Figure 2 The diagram shown is a complete flowchart of the device-side lightweight conditional anonymity secure communication method in this embodiment.

[0037] Example 2 This embodiment discloses a device-side lightweight conditional anonymity secure communication system for the medical Internet of Things (IoT), which executes the device-side lightweight conditional anonymity secure communication method for the medical IoT as described in Embodiment 1; Figure 2 As shown, the system includes: Trusted Center (TA) is used to perform system initialization, device registration management, and abnormal device identification tracking. The system initialization is used to establish unified security parameters and a root of trust for the medical IoT system, providing a cryptographic foundation for device registration, anonymous signatures, and conditional traceability. The device registration management is used to generate lightweight anonymous authentication credentials for resource-constrained medical IoT devices, moving complex calculations to the registration stage so that the real-time signature stage only requires lightweight combination calculations. The abnormal device identity tracking is used to restore the true identity of the device by an authorized trusted center in the event of malicious behavior, achieving a balance between privacy protection and security supervision. Medical Internet of Things (MID) devices are used to perform device registration, medical data signature generation, and transmission. The device registration is used to generate a temporary public-private key pair and calculate the device's anonymous identity, device authentication factor, signature auxiliary parameters, and device signature key. The medical data signature generation is used to generate a conditionally anonymous signature strongly bound to the medical data on the device side with extremely low computational overhead, so as to achieve data source authentication, identity anonymization, and content immutability. The sending is used to send the medical data, device anonymous identity, device authentication factor, and conditionally anonymous signature to the medical server. The medical server (MS) is used to receive medical data and conditionally anonymous signatures, and to perform signature verification to determine whether to accept the medical data. The signature verification is used to receive and verify the legality of medical data and conditionally anonymous signatures, confirming the credibility of the data source and the absence of data tampering without obtaining the true identity of the device, and resisting replay attacks.

[0038] Specifically, the medical Internet of Things (MID) devices include wearable electrocardiogram (ECG) monitoring devices, portable blood glucose meters, smart infusion pumps, dynamic blood pressure monitors, or bedside monitoring devices.

[0039] For details on the implementation process of the lightweight conditional anonymity secure communication system for the medical IoT in this embodiment, please refer to Embodiment 1. It will not be described in detail here.

[0040] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A lightweight conditional anonymity secure communication method for device-side communication in the medical Internet of Things (IoT), characterized in that, include: Device registration steps: Medical IoT devices (MIDs) register with the Trusted Center using their unique identifier, generate a temporary public-private key pair, and calculate the device's anonymous identity, device authentication factor, signature auxiliary parameters, and device signature key. The device authentication factor is a cryptographic digest of the temporary public key, the signature auxiliary parameters are the product of the temporary private key and the inverse of the device authentication factor, and the device signature key contains the sum of relevant terms from the temporary private key and the system private key. Furthermore, the device authentication factor participates in both the generation of the signature auxiliary parameters and the verification of the signature. The device registration steps include: 1) Medical IoT devices (MIDs) are registered using their unique identifier (ID) and a temporary private key is randomly selected. Calculate the temporary public key The temporary public key This serves as a randomly bound parameter in subsequent anonymous signatures; where... Choosing an elliptic curve group for the center of trust generator, for group hierarchy, Modulus The multiplication group; 2) Medical IoT device MID calculation includes device anonymity, device authentication factor, signature auxiliary parameters, and device signature key; among which, Device Anonymity ;in, This represents the XOR operation. The system public key; A hash function defined for a trust center; Equipment certification factors ; A hash function defined for a trust center; Signature auxiliary parameters ; Device signing key ,in, This is the system master key; ; 3) Medical IoT device MID storage For use in subsequent medical data signing; Medical data signing steps: After the medical IoT device (MID) collects medical data, it generates a conditional anonymous signature using signature auxiliary parameters, device signature key, medical data, device anonymous identity, temporary public key, and time parameters, and then sends it. The medical data signing step includes: 1) Medical IoT device MID generates current timestamp This is used to resist replay attacks; 2) Medical IoT devices (MIDs) will integrate medical data. Device Anonymity Temporary public key and current timestamp Perform multi-way hashing to obtain the data binding value and the random challenge value; Data binding value ; Random challenge value ; in, , The hash function defined for the center of trust Data collected for medical IoT devices (MID); Used to bind medical data content, Used to generate random challenges; 3) The Medical Internet of Things (MID) device uses a random challenge value to scale the sum of the products of the signature auxiliary parameters, the data binding value, and the device signature key to obtain the signature value. ; 4) Medical IoT device MID generation includes a signature value. Temporary public key and current timestamp Device-side conditional anonymous signature ; 5) Medical IoT device MID transmission To the medical server MS; Signature verification steps: The medical server performs algebraic operations based on the conditional anonymous signature, system public key, device authentication factor, data binding value, and random challenge value to directly recover the temporary public key and verify whether the cryptographic digest of the temporary public key is equal to the device authentication factor to determine whether to accept the medical data. The signature verification step includes: 1) Verify timestamp Does it meet the valid time window requirement? If the condition is met, then the data should be rejected; where, For the current moment, The effective time window width; 2) Perform independent hash reconstruction based on public parameters; the reconstructed hash value includes: ； ； ； 3) By performing a combination operation on the elliptic curve group using the signature value, system public key, device authentication factor, and reconstructed hash value, the temporary public key can be directly obtained. ; ； 4) Verify whether the cryptographic digest of the recovered temporary public key is equal to the device authentication factor; that is, determine... If the data is valid, the source of the medical data is confirmed to be legal, the device identity is valid, and the data has not been tampered with; otherwise, the data is rejected.

2. The device-side lightweight conditional anonymity secure communication method for the medical Internet of Things according to claim 1, characterized in that, The data collected by the medical IoT device (MID) includes health monitoring data such as electrocardiogram (ECG) data, blood glucose data, blood pressure data, and body temperature data.

3. The device-side lightweight conditional anonymity secure communication method for the Internet of Things in healthcare according to any one of claims 1-2, characterized in that, The system initialization step is included before the device registration step: 1) Trusted Center (TA) selects elliptic curve group Generator with group level ; To establish the mathematical foundation of cryptographic operations; 2) The Trusted Center (TA) randomly selects the system master key. Public key of computing system Establish an asymmetric root of trust; among which, Modulus The multiplication group; 3) The Trusted Center (TA) defines multiple sets of hash functions for different output domains, which respectively support identity anonymity, parameter binding, data verification, and challenge generation; Specifically, this includes hash functions. , , , ;in, , ;in, , Indicates the actual identity of medical IoT devices; 4) Trusted Center (TA) publishes global parameters For use by system entities, the master key s is secretly stored for device key generation and anomaly tracing.

4. The device-side lightweight conditional anonymity secure communication method for the Internet of Things in healthcare according to any one of claims 1-2, characterized in that, The system also performs anomaly device identification tracking steps: Under normal circumstances, medical IoT devices (MIDs) operate only with an anonymous device identity. During communication, the medical server cannot obtain the patient's true identity. When malicious upload of false medical data, counterfeiting of medical IoT devices, unauthorized access to the medical platform, or abnormal behavior attacks occur, the medical server submits a tracking request to the Trusted Center (TA). The TA then restores the device's true identity and performs device revocation, security audit, or accountability tracking, achieving a balance between privacy protection and security supervision.

5. The device-side lightweight conditional anonymity secure communication method for the medical Internet of Things according to claim 4, characterized in that, The steps for identifying and tracing abnormal devices include: 1) Trusted Center (TA) obtains the anonymous identity of the device. and the temporary public key in the signature ; 2) The Trusted Center (TA) utilizes the system master key. For temporary public keys Perform scalar multiplication ; Restore by selecting temporary private key With system public key The result of the calculation; 3) The Trusted Center (TA) will anonymize the device's identity. and Perform an XOR operation on the hash value to determine the device's true identity. ; 4) The Trusted Center (TA) performs device removal, security audit, or accountability tracking based on the recovery results.

6. A lightweight conditional anonymity secure communication system for the device side in the medical Internet of Things, characterized in that, The system implementing the device-side lightweight conditional anonymity secure communication method for the Internet of Things in healthcare as described in any one of claims 1-5 includes: Trusted Center (TA) is used to perform system initialization, device registration management, and abnormal device identification tracking. Medical Internet of Things (MID) devices are used to perform device registration, medical data signature generation, and transmission. The medical server (MS) is used to receive medical data and conditional anonymous signatures, and to perform signature verification to determine whether to accept the medical data.

7. The lightweight conditional anonymity secure communication system for the device side of the Internet of Things for medical applications according to claim 6, characterized in that, The medical IoT devices include wearable electrocardiogram monitoring devices, portable blood glucose meters, smart infusion pumps, dynamic blood pressure monitors, or bedside monitoring devices.

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