Ultra-lightweight group key agreement method and system for medical internet of things
By outsourcing modular exponentiation and the Sun Tzu theorem for key distribution in the medical IoT, the computation and communication problems of resource-constrained devices are solved, achieving efficient group key negotiation, which is suitable for large-scale medical IoT.
Patent Information
- Application Number
- CN202611065606.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-07-17
- Publication Date
- 2026-11-06
AI Technical Summary
Existing group key negotiation schemes have high computational overhead and numerous communication rounds in medical IoT, making them difficult to apply to medical devices with extremely limited resources. Furthermore, some lightweight protocols cannot be extended to group scenarios.
The ASCON lightweight authentication encryption algorithm is used to outsource complex modular exponentiation to a high-performance server, and the Sun Tzu theorem is used to achieve broadcast-style secure distribution of group session keys. The random subset generated by resource-constrained devices and the modular exponentiation of server nodes are coordinated through a trust center.
It significantly reduces the computational overhead of resource-constrained devices, achieves low communication overhead and efficient group key negotiation, and is suitable for large-scale medical IoT scenarios.
Smart Images
Figure CN123055273A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of Internet of Things (IoT) security technology, specifically relating to an ultra-lightweight group key negotiation method and system for medical IoT. Background Technology
[0002] With the rapid development of IoT technology, IoT devices have been widely applied in fields such as smart homes, smart healthcare, and intelligent transportation. Especially in the field of smart healthcare, medical IoT devices face strict constraints in terms of computing power, storage resources, and energy supply, while also facing various security threats. Smart healthcare is a health service model that integrates wearable devices, IoT, and mobile internet technologies. It centers on dynamic information interaction, constructing an interconnected ecosystem of all medical elements, including people, objects, and institutions.
[0003] The sensing layer in the medical Internet of Things (IoT) comprises various medical devices and IoT devices. Medical devices include X-ray machines and large medical instruments, while IoT devices include sensors, cameras, RFID tags, and gateways. Due to the long lifespan of medical devices and the emphasis on functionalities over security during the initial construction phase, they often suffer from outdated operating systems, wide geographical distribution, and long-term neglect of security maintenance, making them highly vulnerable to malicious attacks.
[0004] The security of medical IoT devices heavily relies on encrypted communication and authentication, with key negotiation being the core foundation for establishing secure communication channels and achieving secure authentication. Traditional key negotiation protocols largely assume that both communicating parties have similar computing power and resources, but this does not reflect the realities of the IoT. Resource-constrained medical devices, when faced with highly complex and computationally intensive operations such as modular exponentiation, Weil pairing, and dot product, require processing times far exceeding those of powerful servers. Therefore, a group key negotiation scheme that can alleviate the computational burden on medical devices is needed.
[0005] In existing technologies, group key negotiation schemes can be mainly divided into the following categories: Weil-based schemes require a large number of pairing operations, resulting in high computational overhead; elliptic curve scalar multiplication-based schemes also require high computational resources; and Diffie-Hellman extension-based schemes typically require multiple rounds of interaction, leading to high communication overhead. None of these schemes are suitable for resource-constrained medical IoT scenarios. While some lightweight protocols achieve point-to-point authentication and key negotiation, they cannot be extended to group scenarios, limiting their application scope. Summary of the Invention
[0006] This invention aims to overcome the problems of existing group key negotiation schemes based on complex mathematical operations (such as bilinear pairing, elliptic curve dot product, discrete logarithms, etc.), which suffer from high computational overhead, numerous communication rounds, difficulty in adapting to resource-constrained medical IoT devices, and the inability of some lightweight protocols to be extended to group scenarios. It provides an ultra-lightweight group key negotiation method and system for medical IoT that outsources complex modular exponentiation operations to high-performance servers, utilizes ASCON (Lightweight Authentication Encryption Algorithm) for efficient subset generation using ultra-lightweight authentication encryption, and leverages the CRT (Central Theorem of the Grand Historian) for secure broadcast distribution of group session keys.
[0007] To achieve the above-mentioned objectives, the present invention adopts the following technical solution:
[0008] An ultra-lightweight group key negotiation method for medical IoT includes the following steps:
[0009] S1, System Initialization:
[0010] A trusted third party generates system parameters, assigns a coprime modulus as a private key to each group member, and each server node selects its own private key and broadcasts the public key parameters.
[0011] S2, Subset Generation:
[0012] A random subset is generated by randomly selecting a resource-constrained device, which is then encrypted using the ASCON lightweight authentication encryption algorithm and broadcast to other devices in the group. Other devices decrypt the subset, obtain the subset, and add elements to the subset to generate a new subset.
[0013] S3, Session Key Generation:
[0014] The selected resource-constrained device serves as the trust center. It encrypts the new subset using ASCON and sends it to a trusted third party. Each server node performs a modular exponentiation operation based on its own private key and broadcasts the calculation result. The trusted third party decrypts the new subset, selects the corresponding calculated value from the broadcast results of each server node based on the new subset, and multiplies the selected calculated values together to obtain the session key.
[0015] S4, Session Key Distribution:
[0016] A trusted third party uses the Sun Tzu Theorem to calculate the distribution parameters and generate a verification value. The distribution parameters and verification value are then broadcast to all group members. Each group member uses their own private key to recover the session key and verifies the key's correctness using the verification value.
[0017] Preferably, in step S1, the system initialization phase specifically includes the following process:
[0018] A trusted third party selects a cyclic group of order q. ,as well as As The generator; at the same time, it also generates integers. and z, and randomly select Number Belongs to a cyclic group ,in For all A trusted third party selects a large prime number p and generates a set of pairwise coprime private keys. Each of them Let i = 1, 2, ..., m+k, where m+k is the total number of members; then, a trusted third party calculates the corresponding parameters. and ,in , ,and Subsequently, trusted third-party computing ,as well as Finally, a trusted third party transmits each [item] through a secure channel. Transmitted to the corresponding member.
[0019] As a preferred option, trusted third parties will also transmit data via secure channels. Transmit the private key used for ASCON encryption. The private key obtained is the same.
[0020] Preferably, in step S2, during the subset generation process, the resource-constrained device utilizes a shared key within the group. ASCON encryption is applied to a random subset S to generate ciphertext. And broadcast, other resource-constrained devices use the shared key to decrypt and obtain the random subset S; based on the random value T of the session start timestamp and the preset increment. Calculate new elements ,like If it already exists in the current random subset S, recalculate until it no longer exists; then... Adding subset S to form a new subset .
[0021] Preferably, in step S2, during the subset generation process, each group member calculates the new subset. The product of the public key parameters corresponding to all elements in the array = ,like If the value is 1, the current subset generation process is terminated and restarted.
[0022] Preferably, in step S3, during the session key generation process, the trust center... The new subset After concatenating the timestamp t, the ciphertext is generated using ASCON encryption. And send it to a trusted third party; server node , ,..., Each server node Select private key ,in ={1,2, q-1}, It is the multiplicative identity group of integers modulo q, consisting of all residue classes coprime to q, and forms a finite commutative group under multiplication modulo q. Each server node conduct , ,..., The modular exponentiation yields n values, which are then broadcast within the group; a trusted third party decrypts the data to obtain the new subset. Based on the values of each element in the new subset, the corresponding index values in the broadcast results of each server node are selected and multiplied together to obtain the session key. = .
[0023] Preferably, in step S4, during the session key distribution process, a trusted third party obtains... Select a random number r from the given data and calculate the parameters. = , It is the sum of all the reconstruction coefficients of Sun Tzu's Theorem, used in conjunction with Sun Tzu's Theorem to achieve broadcast-style secure distribution of session keys. , It is the multiplicative identity group modulo p; set a verification value. ,in, For a secure hash function, V is the current group member set; a trusted third party will { , Broadcast to all group members;
[0024] Each group member uses their own private key. Recover the session key using modular arithmetic. Calculate the hash value of the recovered session key. and the verification value If the comparison is inconsistent, the session will be terminated.
[0025] This invention also provides an ultra-lightweight group key negotiation system for the Internet of Things in healthcare, comprising:
[0026] The resource-constrained device module is used to perform ASCON lightweight authentication encryption to generate random subsets and participate in the subset generation process;
[0027] The server module is used to perform modular exponentiation, providing the computational basis for session key generation, and outsourcing the computational tasks of medical devices whose computing resources do not meet the requirements of modular exponentiation.
[0028] The Trust Center module is used to collect encrypted random numbers from resource-constrained devices and perform unified decryption processing, and is responsible for secure communication with trusted third parties;
[0029] A trusted third-party module is used for generating system initialization parameters, calculating session keys, and distributing Sun Tzu's Theorem.
[0030] Preferably, the server module performs modular exponentiation, while the resource-constrained device module does not participate in any modular exponentiation. The resource-constrained device module only performs ASCON encryption / decryption operations and modular exponentiation operations. The ASCON encryption / decryption operations performed by the resource-constrained device module and the modular exponentiation operations performed by the server module are carried out in parallel.
[0031] Preferably, the resource-limited device module comprises 90% of the total number of group members, and the resource-limited device module includes any one or more of the following: heart rate monitor, blood glucose monitor, blood pressure monitor, and body temperature sensor.
[0032] Compared with the prior art, the beneficial effects of this invention are: (1) This invention outsources complex modular exponentiation operations to high-performance servers, avoiding high computational costs for resource-constrained medical devices, and significantly reducing the computational overhead of medical devices; when the total number of members is 200, the key negotiation time of medical devices is only 1.5ms, accounting for 17.5% of the total overhead; (2) This invention uses the Sun Tzu theorem to distribute group session keys, realizing efficient key distribution in broadcast mode, with low communication overhead, and the security has been theoretically proven; (3) This invention uses the ASCON ultra-lightweight authentication encryption algorithm for subset generation, which is certified by the NIST standard, providing high security while maintaining extremely low computational overhead; (4) Compared with the existing group key negotiation scheme based on Weil pairing or dot product operations, the computational efficiency and communication efficiency of this invention are significantly improved; when the number of members reaches 2000, the total computation time is about 1 second, and resource-constrained devices account for 90% of the total number of members, which is more in line with the actual medical IoT scenario. Attached Figure Description
[0033] Figure 1 This is a schematic diagram of the overall process of the ultra-lightweight group key negotiation method for medical IoT in this invention. Figure 2 This is a flowchart of the subset generation stage in this invention; Figure 3 This is a flowchart of the session key generation stage in this invention; Figure 4 This is a flowchart of the session key distribution phase in this invention. Detailed Implementation
[0034] To more clearly illustrate the embodiments of the present invention, specific implementation methods will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.
[0035] like Figure 1 As shown, this invention provides a privacy-preserving semantic indexing and verifiable retrieval method for blockchain, comprising the following steps:
[0036] An ultra-lightweight group key negotiation method for medical IoT includes the following steps:
[0037] S1, System Initialization:
[0038] A trusted third party generates system parameters, assigns a coprime modulus as a private key to each group member, and each server node selects its own private key and broadcasts the public key parameters; assuming... IoT devices with limited computing power cannot efficiently perform modular exponential operations. , , ..., Designated as a high-performance server node, it possesses powerful computing capabilities.
[0039] S2, Subset Generation:
[0040] A random subset is generated by randomly selecting a resource-constrained device, which is then encrypted using the ASCON lightweight authentication encryption algorithm and broadcast to other devices in the group. Other devices decrypt the subset, obtain the subset, and add elements to the subset to generate a new subset.
[0041] S3, Session Key Generation:
[0042] The selected resource-constrained device serves as the trust center. It encrypts the new subset using ASCON and sends it to a trusted third party. Each server node performs a modular exponentiation operation based on its own private key and broadcasts the calculation result. The trusted third party decrypts the new subset, selects the corresponding calculated value from the broadcast results of each server node based on the new subset, and multiplies the selected calculated values together to obtain the session key.
[0043] S4, Session Key Distribution:
[0044] A trusted third party uses the Sun Tzu Theorem to calculate the distribution parameters and generate a verification value. The distribution parameters and verification value are then broadcast to all group members. Each group member uses their own private key to recover the session key and verifies the key's correctness using the verification value.
[0045] Specifically, in step S1, the system initialization phase includes the following processes:
[0046] A trusted third party selects a cyclic group of order q. ,as well as As The generator; at the same time, it also generates integers. and z, and randomly select Number Belongs to a cyclic group ,in For all A trusted third party selects a large prime number p and generates a set of pairwise coprime private keys. Each of them Let i = 1, 2, ..., m+k, where m+k is the total number of members; then, a trusted third party calculates the corresponding parameters. and ,in , ,and Subsequently, trusted third-party computing ,as well as Finally, a trusted third party transmits each [item] through a secure channel. Transmitted to the corresponding member.
[0047] At the same time, trusted third parties will also send information through secure channels to... Transmit the private key used for ASCON encryption. The private key obtained is the same.
[0048] Specifically, such as Figure 2 As shown, in step S2, during the subset generation process, the resource-constrained device Utilize group shared keys ASCON encryption is applied to a random subset S to generate ciphertext. And broadcast, other resource-constrained devices use the shared key to decrypt and obtain the random subset S; based on the random value T of the session start timestamp and the preset increment. Calculate new elements ,like If it already exists in the current random subset S, recalculate until it no longer exists; then... Adding subset S to form a new subset .
[0049] During the subset generation process, each group member calculates the new subset. The product of the public key parameters corresponding to all elements in the array = ,like If the value is 1, the current subset generation process is terminated and restarted.
[0050] Specifically, such as Figure 3 As shown, in step S3, during the session key generation process, the trust center in the resource-constrained device... The new subset After concatenating the timestamp t, the ciphertext is generated using ASCON encryption. And send it to a trusted third party; server node , , ..., Each server node Select private key ,in ={1,2, q-1}, It is the multiplicative identity group modulo q. Each server node conduct , ,..., The modular exponentiation yields n values, which are then broadcast within the group; a trusted third party decrypts the data to obtain the new subset. Based on the values of each element in the new subset, the corresponding index values in the broadcast results of each server node are selected and multiplied together to obtain the session key. = .
[0051] Specifically, such as Figure 4 As shown, in step S4, during the session key distribution process, a trusted third party obtains... Select a random number r from the given data and calculate the parameters. = , It is the sum of all the reconstruction coefficients of Sun Tzu's Theorem, used in conjunction with Sun Tzu's Theorem to achieve broadcast-style secure distribution of session keys. , It is the multiplicative identity group modulo p; set a verification value. ,in, For a secure hash function, V is the current group member set; a trusted third party will { , Broadcast to all group members;
[0052] Each group member uses their own private key. Recover the session key using modular arithmetic. Calculate the hash value of the recovered session key. and the verification value If the comparison is inconsistent, the session will be terminated.
[0053] In addition, the present invention also provides an ultra-lightweight group key negotiation system for the medical Internet of Things, comprising:
[0054] The resource-constrained device module is used to perform ASCON lightweight authentication encryption to generate random subsets and participate in the subset generation process;
[0055] The server module is used to perform modular exponentiation, providing the computational basis for session key generation, and outsourcing the computational tasks of medical devices whose computing resources do not meet the requirements of modular exponentiation.
[0056] The Trust Center module is used to collect encrypted random numbers from resource-constrained devices and perform unified decryption processing, and is responsible for secure communication with trusted third parties;
[0057] A trusted third-party module is used for generating system initialization parameters, calculating session keys, and distributing Sun Tzu's Theorem.
[0058] The server module performs modular exponentiation, while the resource-constrained device module does not participate in any modular exponentiation. The resource-constrained device module only performs ASCON encryption / decryption operations and modular exponentiation operations. The ASCON encryption / decryption operations performed by the resource-constrained device module and the modular exponentiation operations performed by the server module are carried out in parallel.
[0059] The resource-constrained device module comprises 90% of the total number of group members, and includes any one or more of the following: heart rate monitor, blood glucose monitor, blood pressure monitor, and body temperature sensor.
[0060] Based on the technical solution of this invention, the implementation process of this invention in practical applications will be illustrated through the following case scenarios. The specific application implementation scheme is as follows:
[0061] System parameter settings: Group size n = 200, with resource-constrained devices numbered m = 180 (approximately 90% of the total), high-performance servers numbered k = 3, and subset size z = 64. Set size n = 512. A trusted third party selects large prime numbers p and q to generate a cyclic group G. Coprime moduli are assigned to the 200 group members. , , ..., Once the system parameters are prepared, the protocol execution phase begins.
[0062] The preparation phase took a total of 6.352ms, of which approximately 5.632ms were used for modular exponentiation (mainly for generating...). , ..., (This part can be generated offline by a trusted third party and is not included in the protocol time overhead); during the key negotiation phase, the medical device's overhead is 1.5ms (from ASCON encryption operations), and the total overhead is 8.567ms, with most of the overhead coming from the server's modular exponentiation operations. Since the server can perform modular exponentiation operations simultaneously while the medical device performs encryption and decryption operations, the actual calculation time can be further shortened.
[0063] The computational complexity comparison between the present invention and existing technical solutions is as follows: When the total number of members N=200, the traditional Chinese medicine device in the present invention performs 3 ASCON operations, the TTP server performs 65 modular multiplications, 1 hash operation, 1 ASCON operation, 200 additions, and 1 modular inverse operation, and the server performs 512 modular exponentiation operations, with a total time of 8.567ms. When the number of members reaches 2000, the running time of the present invention is approximately 1 second. As the number of members increases, the performance advantage of the present invention becomes more and more obvious, making it particularly suitable for large-scale medical IoT scenarios.
[0064] Regarding communication overhead, the present invention treats resource-constrained devices as a single unit and uses ASCON to generate a common subset. Assume there are m resource-constrained devices and k resource-unconstrained devices. The communication overhead for a resource-constrained device is 1 (requiring only one broadcast), the communication overhead for the session key generation stage is k+1, and the communication overhead for the final session key acquisition process is m+k+1. Therefore, the total communication overhead is O(n).
[0065] This invention has the following technical advantages:
[0066] 1. This invention takes into account the special characteristics of resource-constrained devices in the medical Internet of Things (IoT). By outsourcing modular exponentiation and introducing the Sun Tzu theorem, the computational overhead of resource-constrained devices is significantly reduced. When the total number of members is 200, the key negotiation time for medical devices is only 1.5ms.
[0067] 2. This invention uses ASCON to generate subsets, which balances security and lightweight design.
[0068] 3. This invention has a wide range of applications and can be extended to other IoT scenarios with limited resources, such as smart homes, industrial IoT, and drone networking.
[0069] The above description is merely a detailed explanation of preferred embodiments and principles of the present invention. For those skilled in the art, there may be changes in specific implementation methods based on the ideas provided by the present invention, and these changes should also be considered within the scope of protection of the present invention.
Claims
1. An ultra-lightweight group key negotiation method for the Internet of Things in healthcare, characterized in that, Includes the following steps: S1, System Initialization: A trusted third party generates system parameters and assigns a coprime modulus as a private key to each group member. Each server node selects its own private key and broadcasts the public key parameters. S2, Subset Generation: A random subset is generated by randomly selecting a resource-constrained device, which is then encrypted using the ASCON lightweight authentication encryption algorithm and broadcast to other devices in the group. Other devices decrypt the subset, obtain the subset, and add elements to the subset to generate a new subset. S3, Session Key Generation: The selected resource-constrained device serves as the trust center. It encrypts the new subset using ASCON and sends it to a trusted third party. Each server node performs a modular exponentiation operation based on its own private key and broadcasts the calculation result. The trusted third party decrypts the new subset, selects the corresponding calculated value from the broadcast results of each server node based on the new subset, and multiplies the selected calculated values together to obtain the session key. S4, Session Key Distribution: A trusted third party uses the Sun Tzu Theorem to calculate the distribution parameters and generate a verification value. The distribution parameters and verification value are then broadcast to all group members. Each group member uses their own private key to recover the session key and verifies the key's correctness using the verification value.
2. The ultra-lightweight group key negotiation method for medical IoT according to claim 1, characterized in that, In step S1, the system initialization phase specifically includes the following processes: A trusted third party selects a cyclic group of order q. ,as well as As The generator; at the same time, it also generates integers. and z, and randomly select Number Belongs to a cyclic group ,in For all A trusted third party selects a prime number p and generates a set of pairwise coprime private keys. Each of them Let i = 1,2,…,m+k, where m+k is the total number of members; then, a trusted third party calculates the corresponding parameters. and ,in , ,and ; Subsequently, trusted third-party computing ,as well as Finally, a trusted third party transmits each [item] through a secure channel. Transmitted to the corresponding member.
3. The ultra-lightweight group key negotiation method for medical IoT according to claim 2, characterized in that, In step S1, the system initialization phase further includes the following processes: Trusted third parties communicate with IoT devices via secure channels Transmit the private key used for ASCON encryption.
4. The ultra-lightweight group key negotiation method for medical IoT according to claim 3, characterized in that, In step S2, during the subset generation process, the resource-constrained device utilizes the group-shared key. ASCON encryption is applied to a random subset S to generate ciphertext. And broadcast, other resource-constrained devices use the shared key to decrypt and obtain the random subset S; A random value T based on the session start timestamp and a preset increment. Calculate new elements ,like If it already exists in the current random subset S, recalculate until it no longer exists; then... Adding subset S to form a new subset .
5. The ultra-lightweight group key negotiation method for medical IoT according to claim 4, characterized in that, In step S2, during the subset generation process, each group member calculates the new subset. The product of the public key parameters corresponding to all elements in the array = ,like If the value is 1, the current subset generation process is terminated and restarted.
6. The ultra-lightweight group key negotiation method for medical IoT according to claim 5, characterized in that, In step S3, during the session key generation process, the trust center The new subset After concatenating the timestamp t, the ciphertext is generated using ASCON encryption. And send it to a trusted third party; server node , ,..., Each server node Select private key ,in ={1,2, q-1}, It is the multiplicative identity group modulo q; Each server node conduct , ,..., The modular exponentiation yields n values, which are then broadcast within the group; Trusted third party decrypts to obtain the new subset Based on the values of each element in the new subset, the corresponding index values in the broadcast results of each server node are selected and multiplied together to obtain the session key. = .
7. The ultra-lightweight group key negotiation method for medical IoT according to claim 6, characterized in that, In step S4, during the session key distribution process, a trusted third party obtains... Select a random number r from the given data and calculate the parameters. = , It is the sum of all the reconstruction coefficients of Sun Tzu's Theorem, used in conjunction with Sun Tzu's Theorem to achieve broadcast-style secure distribution of session keys. , It is the multiplicative identity group of integers modulo p; Set verification value ,in, For a secure hash function, V is the current group member set; a trusted third party will { , Broadcast to all group members; Each group member uses their own private key. Recover the session key using modular arithmetic. Calculate the hash value of the recovered session key. and the verification value If the comparison is inconsistent, the session will be terminated.
8. A lightweight group key negotiation system for the Internet of Things (IoT) in healthcare, used to implement the lightweight group key negotiation method for the IoT in healthcare as described in any one of claims 1-7, characterized in that, The ultra-lightweight group key negotiation system for the medical Internet of Things includes: The resource-constrained device module is used to perform ASCON lightweight authentication encryption to generate random subsets and participate in the subset generation process; The server module is used to perform modular exponentiation, providing the computational basis for session key generation, and outsourcing the computational tasks of medical devices whose computing resources do not meet the requirements of modular exponentiation. The Trust Center module is used to collect encrypted random numbers from resource-constrained devices and perform unified decryption processing, and is responsible for secure communication with trusted third parties; A trusted third-party module is used for generating system initialization parameters, calculating session keys, and distributing Sun Tzu's Theorem.
9. The ultra-lightweight group key negotiation system for medical IoT according to claim 8, characterized in that, The server module performs modular exponentiation, while the resource-constrained device module does not participate in any modular exponentiation. The resource-constrained device module only performs ASCON encryption / decryption operations and modular exponentiation operations. The ASCON encryption / decryption operations performed by the resource-constrained device module and the modular exponentiation operations performed by the server module are carried out in parallel.
10. The ultra-lightweight group key negotiation system for medical IoT according to claim 9, characterized in that, The resource-constrained device module comprises 90% of the total number of group members, and includes any one or more of the following: heart rate monitor, blood glucose monitor, blood pressure monitor, and body temperature sensor.