WO2022141014A1 - Procédé de moyennage de sécurité basé sur des données multi-utilisateurs - Google Patents

Procédé de moyennage de sécurité basé sur des données multi-utilisateurs Download PDF

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Publication number
WO2022141014A1
WO2022141014A1 PCT/CN2020/140586 CN2020140586W WO2022141014A1 WO 2022141014 A1 WO2022141014 A1 WO 2022141014A1 CN 2020140586 W CN2020140586 W CN 2020140586W WO 2022141014 A1 WO2022141014 A1 WO 2022141014A1
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WO
WIPO (PCT)
Prior art keywords
data
ciphertext
user
target user
extended
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Ceased
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PCT/CN2020/140586
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English (en)
Chinese (zh)
Inventor
张鹏
赵威
孙小强
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Shenzhen University
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Shenzhen University
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Priority to PCT/CN2020/140586 priority Critical patent/WO2022141014A1/fr
Publication of WO2022141014A1 publication Critical patent/WO2022141014A1/fr
Anticipated expiration legal-status Critical
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    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/602—Providing cryptographic facilities or services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/008—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols involving homomorphic encryption
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L2209/00—Additional information or applications relating to cryptographic mechanisms or cryptographic arrangements for secret or secure communication H04L9/00
    • H04L2209/76—Proxy, i.e. using intermediary entity to perform cryptographic operations

Definitions

  • the present application relates to the technical field of computer network applications, and in particular to a secure averaging method based on multi-user data.
  • the embodiments of the present application provide a secure averaging method based on multi-user data, so as to overcome the problem in the prior art that the server cannot realize the averaging of multi-user data while protecting the privacy of user data.
  • the embodiment of the present application provides a method for calculating a safe mean value based on multi-user data, including:
  • the ciphertext of a preset number of target users is respectively expanded to obtain the expanded ciphertext corresponding to each target user, and the decryption key of the expanded ciphertext is the corresponding decryption key of each target user.
  • private key
  • the ciphertext corresponding to the mean value data is obtained by the first target user by calculating the mean value based on the user plaintext data, and the user plaintext data is each target user based on each target user.
  • the private key corresponding to the user is obtained by decrypting the accumulated extended ciphertext, and the first target user is any target user among the target users.
  • all the extended ciphertexts are accumulated and sent to each target user, including:
  • All the first extended ciphertexts are accumulated and sent to each target user.
  • the ciphertext that includes the mean value data fed back by the first target user includes:
  • a difference is made between the ciphertext containing the mean value data fed back by the first target user and the second ciphertext to obtain the ciphertext corresponding to the mean value data.
  • the method further includes:
  • the embodiment of the present application also adopts a method for calculating a safe mean value based on multi-user data, including:
  • the extended ciphertext includes a first ciphertext corresponding to random disturbance data
  • the plaintext data obtained by obtaining a preset number of target users to decrypt the extended ciphertext respectively include:
  • Receive plaintext data sent by other target users where the plaintext data is obtained by other target users decrypting the extended ciphertext based on their own private keys, and the plaintext data includes the random perturbation data.
  • calculating the average value of plaintext data of all target users based on the preset number to obtain average value data including:
  • the total plaintext data is averaged based on the preset number to obtain the average data.
  • the method before receiving the extended ciphertext sent by the server, the method further includes:
  • the user data corresponding to the current target user is homomorphically encrypted based on the public key of the current target user to obtain a ciphertext, and the ciphertext is sent to the server.
  • An embodiment of the present application further provides an electronic device, including: a memory and a processor, the memory and the processor are connected in communication with each other, the memory stores computer instructions, and the processor executes the computer instructions, thereby executing the multi-user data-based secure averaging method provided by the embodiments of the present application.
  • the embodiments of the present application further provide a computer-readable storage medium, where the computer-readable storage medium stores computer instructions, and the computer instructions are used to cause the computer to execute the multi-user data-based security requirements provided by the embodiments of the present application mean method.
  • the embodiment of the present application provides a secure mean value method based on multi-user data.
  • the server separately expands the ciphertext of a preset number of target users through the public key based on the multi-key fully homomorphic encryption algorithm, and obtains the corresponding value of each target user.
  • the decryption key of the extended ciphertext is the private key corresponding to each target user; all the extended ciphertexts are accumulated and sent to each target user; the current target user obtains a preset number of target users respectively for the extended ciphertext Perform the decrypted plaintext data; and average the plaintext data of all target users based on the preset number to obtain the average value data; then perform homomorphic encryption on the average value data based on the public key of the current target user to obtain the ciphertext containing the average value data, and sent to the server.
  • the server obtains the ciphertext corresponding to the mean value, which not only ensures user privacy and security on the server side, but also obtains the mean value ciphertext of multi-user data.
  • FIG. 1 is a schematic diagram of an interaction process for security averaging based on multi-user data in an embodiment of the present application
  • FIG. 2 is a schematic diagram of another interactive process of multi-user data-based security averaging in an embodiment of the present application
  • FIG. 3 is a schematic structural diagram of an electronic device in an embodiment of the present application.
  • an embodiment of the present application provides a secure averaging system based on multi-user data, the system includes a server and a plurality of target users, wherein the first target user performing the average calculation is any one of all the target users,
  • the server and the first target user are taken as examples to describe in detail the secure averaging method based on multi-user data provided by the embodiment of the present application.
  • the server side is used to execute the steps From S101 to step S103
  • the first target user terminal is used to execute steps S201 to S204.
  • the multi-key fully homomorphic encryption scheme based on the multi-key fully homomorphic encryption scheme implements the secure averaging protocol for multi-user data.
  • the specific background of the multi-user secure averaging protocol is as follows:
  • the corresponding ciphertexts are respectively Denoted as C 1 , C 2 ,...,C n , the data is stored in the server.
  • the secure averaging protocol is executed between the cloud server and users U 1 , U 2 ,...,U n , and aims to calculate all data in a given data cluster under the premise that the ciphertext is known without revealing the plaintext of each user object mean the corresponding ciphertext.
  • the multi-key fully homomorphic encryption algorithm can be selected from other existing homomorphic encryption algorithms in the prior art, such as the BGV type multi-key fully homomorphic encryption method. Not limited to this.
  • the security averaging method based on multi-user data specifically includes the following steps:
  • Step S101 Expand the ciphertext of a preset number of target users based on the public key of the multi-key fully homomorphic encryption algorithm, respectively, to obtain the expanded ciphertext corresponding to each target user, and the decryption key of the expanded ciphertext is corresponding to each target user. 's private key.
  • the ciphertext is obtained after each target user outputs the public and private keys corresponding to each target user by running the above-mentioned CZW.KGen algorithm, and performs homomorphic encryption on the target user's user data by using the respective public keys.
  • the public key is the public key obtained by running the above CZW.Setup algorithm.
  • Step S102 All the extended ciphertexts are accumulated and sent to each target user. Specifically, the server runs the algorithm CZW.CTExt to expand the ciphertexts C 1 , C 2 , . . . , C n of each user to be make That is, the private key corresponding to the expanded ciphertext is the private key corresponding to each target user. n represents the number of target users.
  • Step S201 Receive the extended ciphertext sent by the server, where the extended ciphertext is obtained by the server expanding and accumulating the ciphertexts of all target users based on the public key of the multi-key fully homomorphic encryption algorithm. Specifically, each target user receives the extended ciphertext sent by the server respectively.
  • the above-mentioned first target user receives the extended ciphertext sent by the server as an example for description.
  • Step S202 Acquire plaintext data obtained by decrypting the extended ciphertext by a preset number of target users respectively. Specifically, after each target user outputs the user's public and private keys through the above-mentioned CZW.KGen algorithm, respectively, uses the user's private key to decrypt the above-mentioned extended ciphertext to obtain a part of plaintext data respectively.
  • Step S203 Average the plaintext data of all target users based on a preset number to obtain average data.
  • the first target user can obtain the mean value data corresponding to the plaintext data of all the target users by accumulating the plaintext data obtained by decrypting all the target users and then calculating the average value.
  • Step S204 Homomorphically encrypt the mean value data based on the public key of the current target user, obtain a ciphertext including the mean value data, and send it to the server.
  • the current target user is the above-mentioned first target user
  • the ciphertext corresponding to the mean value data is calculated by running the CZW.Enc algorithm, that is, the above-mentioned first target user encrypts the mean value data by using its corresponding public key, Obtain the ciphertext corresponding to the mean data.
  • Step S103 Receive the ciphertext including the mean value data fed back by the first target user, the ciphertext corresponding to the mean value data is obtained by the first target user based on the average value of the user plaintext data, and the user plaintext data is each target user based on the corresponding target users.
  • the first target user is obtained by decrypting the accumulated extended ciphertext with the private key, and the first target user is any target user among the target users.
  • the server can obtain the mean value of the plaintext data of all target users.
  • the server can obtain the ciphertext corresponding to the mean value, which not only ensures the privacy and security of users on the server side, but also obtains the mean ciphertext of multi-user data.
  • step S102 specifically includes the following steps:
  • Step S11 Acquire random disturbance data.
  • the random disturbance data is a random binary vector r selected by the server.
  • the random disturbance data can also be randomly selected by the server from preset random disturbance data.
  • this application is not limited to this.
  • Step S12 Based on the random perturbation data, perform homomorphic encryption on the random perturbation data with a public key to obtain a first ciphertext corresponding to the random perturbation data. Specifically, the server uses the above-mentioned public key Encrypt the randomly disturbed data to obtain the corresponding first ciphertext
  • Step S13 Accumulate the first ciphertext and each extended ciphertext respectively to obtain the first extended ciphertext. Specifically, the server accumulates a first ciphertext corresponding to the random perturbation data for the extended ciphertext corresponding to each target user, respectively, to obtain the first extended ciphertext, so that each first extended ciphertext contains perturbed data, so as to obtain the first extended ciphertext. Increase the security of ciphertext data.
  • Step S14 All the first extended ciphertexts are accumulated and sent to each target user. Specifically, the server obtains by accumulating all the first extended ciphertexts and send to each target user.
  • each target user decrypts the extended ciphertext using his own private key, and obtains a result containing the first ciphertext corresponding to the random disturbance data.
  • Plaintext data for randomly perturbed data.
  • each target user cannot obtain the real plaintext data without knowing the random perturbation data, thus further ensuring the privacy of the plaintext data on the target user side, and thus cannot obtain the real plaintext data.
  • the information of the mean data ensures the two-way security of the mean value on the target user side and the server side.
  • step S202 specifically includes the following steps: step:
  • Step S21 Decrypt the extended ciphertext based on the private key of the current target user to obtain current plaintext data, where the current plaintext data includes random disturbance data.
  • Step S22 Receive plaintext data sent by other target users, where the plaintext data is obtained by other target users by decrypting the extended ciphertext based on their own private keys, and the plaintext data includes random disturbance data. Specifically, each target user decrypts the extended ciphertext with their own private key to obtain the corresponding plaintext data, and then sends the respective plaintext data to the current target user participating in the mean calculation, that is, the above-mentioned first target user. A target user processes all plaintext data to obtain mean data.
  • the first ciphertext corresponding to the random perturbation data is included in the extended ciphertext as an example. In practical applications, if the server does not add the first ciphertext corresponding to the random perturbation data.
  • the plaintext data decrypted by the target user does not contain random perturbation data.
  • step S203 specifically includes the following steps:
  • Step S23 Accumulate the current plaintext data and the plaintext data corresponding to other target users to obtain total plaintext data. Specifically, after receiving the plaintext data sent by other target users, the above-mentioned first target user participating in the mean value calculation accumulates the plaintext data decrypted by all target users to obtain the total plaintext data including random disturbance data, that is, each target user. Users U 1 , U 2 ,...,U n run the above CZW.Dec algorithm respectively to decrypt using their own private key, and then accumulate all the decryption results to obtain the total plaintext data g.
  • Step S24 Average the total plaintext data based on the preset number to obtain average data. Specifically, the first target user calculates the mean value with the error term according to the number of all target users Then encrypt by running the CZW.Enc algorithm get ciphertext sent to the server.
  • step S103 specifically includes the following steps:
  • Step S15 Obtain the public key corresponding to the first target user.
  • the first target user is sending the above ciphertext At the same time, it sends its own public key to the server.
  • Step S16 Homomorphically encrypt the random disturbance data based on the public key to obtain a second ciphertext corresponding to the random disturbance data.
  • the server obtains its corresponding ciphertext C i (r) by encrypting the random vector r selected in the above step S11 by using the public key corresponding to the first target user.
  • Step S17 Make a difference between the ciphertext containing the mean data and the second ciphertext fed back by the first target user to obtain the ciphertext corresponding to the mean data. server by computing the mean of the data the corresponding ciphertext.
  • the server can obtain the ciphertext corresponding to the mean value of the plaintext data; on the other hand, by adding error disturbance data to the server, the user cannot directly obtain the mean value information.
  • the user's two-way security is averaged, which protects the user's privacy from being leaked.
  • Table 1 shows the execution flow of the secure averaging protocol constructed by adopting the above-mentioned multi-user data-based secure averaging method according to the embodiment of the present application, wherein the server is a cloud server, and the user is the above-mentioned first target user participating in the averaging calculation.
  • the cloud servers and users of the above-mentioned protocols honestly perform the protocol operations, wherein the cloud servers are responsible for the data objects m 1 , m 2 ,..., m n and O (i) belonging to the data cluster ⁇ . the corresponding mean is curious, the user is curious about the mean.
  • the cloud server without the private keys sk 1 , sk 2 ,..., sk n cannot obtain the data objects m 1 , m 2 ,... from the ciphertext. ,m n , mean value with error term and mean Information.
  • users are calculating When the random binary vector r is introduced, the user cannot pass the average value with the error term when he is uncertain about the value of r. to infer the mean Information.
  • the above-mentioned secure averaging protocol is safe under the semi-honest model, and the security of user privacy data can be guaranteed on both the user side and the server side.
  • the electronic device may include a processor 901 and a memory 902, where the processor 901 and the memory 902 may be connected by a bus or in other ways. Take bus connection as an example.
  • the processor 901 may be a central processing unit (Central Processing Unit, CPU).
  • the processor 901 may also be other general-purpose processors, digital signal processors (Digital Signal Processors, DSPs), application specific integrated circuits (Application Specific Integrated Circuits, ASICs), Field-Programmable Gate Arrays (Field-Programmable Gate Arrays, FPGAs) or Other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components and other chips, or a combination of the above types of chips.
  • DSPs Digital Signal Processors
  • ASICs Application Specific Integrated Circuits
  • FPGAs Field-Programmable Gate Arrays
  • Other programmable logic devices discrete gate or transistor logic devices, discrete hardware components and other chips, or a combination of the above types of chips.
  • the memory 902 can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions/modules corresponding to the methods in the method embodiments of the present application.
  • the processor 901 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 902, ie, implements the methods in the above method embodiments.
  • the memory 902 may include a storage program area and a storage data area, wherein the storage program area may store an operating system and an application program required by at least one function; the storage data area may store data created by the processor 901 and the like. Additionally, memory 902 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 902 may optionally include memory located remotely from processor 901, which may be connected to processor 901 via a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • One or more modules are stored in the memory 902, and when executed by the processor 901, perform the methods in the above method embodiments.
  • the storage medium may be a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a flash memory (Flash Memory), a hard disk (Hard Disk Drive) , abbreviation: HDD) or solid-state drive (Solid-State Drive, SSD), etc.; the storage medium may also include a combination of the above-mentioned types of memories.

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Abstract

L'invention concerne un procédé de moyennage de sécurité basé sur des données multi-utilisateurs, comprenant : un serveur développant respectivement, sur la base d'un algorithme de chiffrement entièrement homomorphique à clé multiple, un texte chiffré d'un nombre prédéfini d'utilisateurs cibles au moyen d'une clé publique pour obtenir un texte chiffré développé correspondant à chaque utilisateur cible (S101) ; l'accumulation de tous les textes chiffrés développés, puis leur envoi à chaque utilisateur cible (S102) ; l'acquisition, par l'utilisateur cible actuel, des données de texte en clair après que le nombre prédéfini d'utilisateurs cibles ont déchiffré les textes chiffrés développés, respectivement (S202) ; le moyennage des données de texte en clair de tous les utilisateurs cibles sur la base du nombre prédéfini pour obtenir des données moyennes (S203) ; puis la réalisation d'un chiffrement homomorphique sur les données moyennes sur la base de la clé publique de l'utilisateur cible actuel pour obtenir un texte chiffré comprenant les données moyennes, et l'envoi du texte chiffré au serveur (S204). De cette manière, l'algorithme de chiffrement homomorphique est utilisé, et le serveur interagit avec de multiples utilisateurs, de sorte que le serveur obtient le texte chiffré correspondant à la moyenne tandis que les informations de données de texte en clair ne sont pas divulguées, et, par conséquent, la sécurité de confidentialité de l'utilisateur est assurée du côté serveur, et le texte chiffré moyen des données multi-utilisateurs est obtenu.
PCT/CN2020/140586 2020-12-29 2020-12-29 Procédé de moyennage de sécurité basé sur des données multi-utilisateurs Ceased WO2022141014A1 (fr)

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CN116527370A (zh) * 2023-05-10 2023-08-01 北京龙腾佳讯科技股份公司 一种安全多方计算数据方差的方法及系统
CN117254908A (zh) * 2023-11-10 2023-12-19 成方金融科技有限公司 一种云端数据存储方法、装置、设备及介质
CN117434907A (zh) * 2023-12-18 2024-01-23 广东科伺智能科技有限公司 基于CoDeSys控制器的伺服驱动器数量控制方法及设备
CN119892335A (zh) * 2024-11-14 2025-04-25 海南大学 一种云计算场景下基于张量链分解的同态运算方法及装置
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CN110147681A (zh) * 2019-04-02 2019-08-20 西安电子科技大学 一种支持灵活访问控制的隐私保护大数据处理方法及系统

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CN116527370A (zh) * 2023-05-10 2023-08-01 北京龙腾佳讯科技股份公司 一种安全多方计算数据方差的方法及系统
CN117254908A (zh) * 2023-11-10 2023-12-19 成方金融科技有限公司 一种云端数据存储方法、装置、设备及介质
CN117254908B (zh) * 2023-11-10 2024-02-02 成方金融科技有限公司 一种云端数据存储方法、装置、设备及介质
CN117434907A (zh) * 2023-12-18 2024-01-23 广东科伺智能科技有限公司 基于CoDeSys控制器的伺服驱动器数量控制方法及设备
CN117434907B (zh) * 2023-12-18 2024-03-22 广东科伺智能科技有限公司 基于CoDeSys控制器的伺服驱动器数量控制方法及设备
CN119892335A (zh) * 2024-11-14 2025-04-25 海南大学 一种云计算场景下基于张量链分解的同态运算方法及装置
CN121619099A (zh) * 2026-02-02 2026-03-06 贵州数据宝网络科技有限公司 一种基于多密钥全同态加密的车险密态建模方法

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