174 results about "Secret share" patented technology

The invention provides a privacy protection multi-task allocation method for an unmanned aerial vehicle crowdsourcing perception system, which combines addition secret sharing and homomorphic encryption to realize bilateral privacy protection of a task side and an unmanned aerial vehicle position. The task requester and the working party split a task position and a UAV departure position into two secret shares [.] 1 and [.] 2 by adopting an additive secret sharing mechanism respectively, and send the two secret shares [.] 1 and [.] 2 to the two independent service parties S1 and S2 respectively; the service party calculates an encrypted square Euclidean distance between the UAV and the task based on the shared share, and generates two distance matrix shares D1 and D2. On the basis, the two service parties cooperatively execute privacy comparison and a minimum index protocol under the optimized Paillier, and multi-task encryption shortest path distribution is realized in combination with a Hungary algorithm. According to the method, addition secret sharing is fused, Paillier encryption and task matching optimization are optimized, the total flight path of the UAV is remarkably reduced while position information privacy of the whole task allocation process is guaranteed, and good safety and deployability are achieved.

The invention discloses an identity authentication method and system based on a national secret algorithm, and the method comprises the steps: building a hierarchical key management system based on a national secret IBE framework, generating a system master key pair through employing an SM2 algorithm, and achieving a decentralized key distribution mechanism; constructing a domain perception differential privacy protection module, defining a privacy budget allocation strategy according to the security level, and adding calibrated Laplace noise to the user identity feature vector; designing a distributed batch matrix multiplication protocol, and decomposing the distributed batch matrix multiplication protocol to a plurality of computing nodes for parallel processing through a secret sharing technology; a zero-knowledge proof verification mechanism is implemented, and identity verification is completed through a commitment scheme based on an SM3 hash algorithm; deploying a self-adaptive key updating strategy, and analyzing threat level change through a threat situation evaluation function; and establishing a secure communication channel based on SM4 symmetric encryption, and performing encryption processing by using the temporary session key. The security and expandability of the system are improved, the privacy of the user is effectively protected, and the system adapts to a dynamically changing network threat environment.

Methods and systems for securely generating secret shares in a distributed manner and distributing those secret shares to cryptographic devices are disclosed. The cryptographic devices can subsequently use these secret shares to perform threshold distributed cryptographic operations (such as encryption or decryption). A threshold number of generating cryptographic devices can each generate their own secret shares. These devices can also each generate partial secret shares that can be combined by receiving cryptographic devices to generate their own respective secret shares. Additionally, the generating devices can generate commitments corresponding to their secret shares. The generating devices can transmit the commitments to the other cryptographic devices and the partial secret shares to their corresponding receiving devices. At a later time, cryptographic devices possessing at least a threshold number of secret shares can collectively perform cryptographic operations using those secret shares.

The sender stores a pair of sender's public and private keys and a public key of the receiver, and carries out the steps of:A—generating a master key, that is a secret shared with the receiver, by key derivation from a current root key and using the sender's private key and the receiver's public key, and updating the root key with the generated master key;B—generating, by key derivation from said master key, a chain of message keys for securing messages to communicate to the receiver;C—generating a next pair of sender's public and private keys, sending the next sender's public key to the receiver and executing again the steps A andB using the next sender's private key and the same receiver's public key;wherein the current root key used in the step A isat a first iteration of step A, an initial root key prestored in the sender, said initial root key being a secret shared with the receiver, andat each subsequent iteration of the step A, the root key that has been updated at the preceding iteration of step A.

The embodiment of the invention discloses a safety calculation method and device for robot cluster collaboration and a medium, and relates to the technical field of safety calculation, and the method comprises the steps: carrying out the participant authentication of a robot cluster and a production enterprise node in advance, and carrying out the dynamic identity verification of each robot after the participant authentication is passed, the operation feature data of each target robot passing verification are extracted and encrypted, and encrypted feature vectors are determined; splitting the encrypted feature vector into a plurality of data fragments, storing the data fragments to a distributed node, obtaining a predetermined task cooperation strategy, and distributing the task cooperation strategy to an edge computing node in a secret sharing manner; executing safety calculation in a ciphertext state through a task cooperation strategy and the plurality of data fragments, determining a joint motion control instruction of each target robot, and generating corresponding zero-knowledge proof data; and after the verification is passed, the encrypted control instruction is distributed through the block chain smart contract, so that safety calculation of robot cluster collaboration is realized.

The invention discloses a privacy protection neural network reasoning method based on multi-point evaluation. According to the method, in the reasoning process of the privacy protection neural network, a given nonlinear function is constructed into a K-segment k-degree polynomial, a safe multiplication tree and a safe remainder tree are established, and then the remainder polynomial is safely evaluated. Due to the fact that the polynomial power needing to be evaluated in the online stage is reduced, the number of self-multiplication terms is reduced, the number of communication rounds of overall polynomial evaluation is reduced, the number of calling times of a secure multiplication triple in secret sharing is reduced, and the overhead of offline pre-calculation is correspondingly reduced. In addition, the hierarchical structure of the secure multiplication tree and the remainder tree supports multi-point input parallel computing, and the parallel computing capability of a modern multi-core processor or a GPU can be fully utilized.

A computer-implemented method of generating shares of a shared secret, wherein each of a group of participants has a respective first secret share of the shared secret, wherein the method is performed by a first participant of the group and comprises: generating a respective blinding share of a shared blinding secret, obtaining at least a threshold number of respective intermediary shares from each of the first group of participants, wherein each respective intermediary share is generated based on a respective blinding share and a respective first secret share; generating an intermediary value based on each of the obtained intermediary shares; and generating a respective second secret share of the shared secret, wherein the respective second secret shared is generated based on the intermediary value and the respective blinding share.

The invention provides a multi-person approval control method, an electronic device, a readable medium and a program product, and the method comprises the steps: responding to the submission of an approval application, generating a process continuing key for the approval application, splitting the process continuing key into n secret shares through a threshold secret sharing algorithm according to a preset (k, n) threshold parameter, and distributing the n secret shares to each approver; receiving an approval instruction submitted by each approver and the signed secret share associated with the approval instruction; verifying the validity of the signed secret share of which the approval instruction is agreed, and determining the signed secret share as an effective share if the verification is passed; when the number of the effective shares reaches or exceeds k, at least k shares are selected from the effective shares, and a process continuing key is restored through a threshold secret sharing algorithm; and performing comparison verification on the restored flow continuing key and a flow continuing key generated during initialization, and triggering execution of a business flow corresponding to the approval application after verification is passed. According to the invention, multi-person examination and approval can be realized, and compulsory compliance and tampering prevention

The invention discloses a non-interactive digital identity authentication method and system for privacy outsourcing calculation, and the method comprises the steps: a user mobile terminal analyzes an identity certificate of a user into a witness vector and generates a secret share, selects an authentication node, generates and transmits an anchor point secret share, a witness vector secret share and a label for the authentication node, and transmits the anchor point secret share, the witness vector secret share and the label to the authentication node; sending a global key secret share and a local key component secret share for generating a tag to the remaining computing nodes; each computing node in the privacy computing cloud cluster performs witness consistency check, if the check is passed, an identity authentication circuit is executed in a secret state to obtain a secret share of an authentication result, and a proof fragment of a simple non-interactive zero-knowledge proof is calculated based on a calculation track executed by the circuit; the authentication nodes gather to obtain a complete proof and recover an authentication result, the proof is delivered to a verification party, the authentication result is pushed to a user mobile terminal, and in the proof generation process, PIOP consistency check needs to be carried out after each round of polynomial commitment is generated.

The invention discloses a quantum security verifiable secret sharing method and device, equipment and a storage medium, is applied to a secret holder, and relates to the technical field of network security, and the method comprises the steps: determining a secret extraction polynomial of a target secret, and determining a target polynomial for determining a share; determining each secret share by using a hidden polynomial, a target secret, a secret extraction polynomial and a target polynomial; determining a fingerprint value based on a polynomial set and the target polynomial; determining a commitment value by using a share distribution polynomial, an auxiliary polynomial and the fingerprint value; sending the secret shares and the target polynomial to each participant; the participant verifies the secret share and the target polynomial; and if the verification is passed, the target secret share passing the verification and the corresponding target polynomial are disclosed, and the Chinese remainder theorem is utilized to carry out secret recovery to obtain the target secret so as to complete the corresponding secret sharing operation. And a verifiable function of secret sharing is realized at low cost.

This invention provides a privacy-preserving community search method and system for graph data streams. It utilizes a secret shared domain comprised of multiple servers. Within this domain, each server can only access the first encrypted data of the graph data stream's list structure and the second encrypted data of the search parameters. Throughout the community search process, the search parameters and all information in the graph data stream except for the time of generation are processed within the encrypted domain and are not leaked to the querying server, thus achieving privacy protection during the community search process for graph data streams.

This invention discloses a password matching method based on equality testing. Each matching server obtains a login password secret share; the difference between the pre-held registration password secret share and the login password secret share is obtained to get a difference secret share; the first equality test problem between the registration password secret share and the login password secret share is transformed into a second equality test problem between the difference secret share and 0; based on addition, multiplication, and inner product calculations under replicated secret sharing, the second equality problem is subjected to ring dimensionality reduction to obtain a third equality test problem; the third equality test problem is solved based on Fermat's Little Theorem to obtain the equality test result, thereby obtaining the password matching result.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for using cryptographic techniques to enhance data security and privacy and increasing computational efficiency in selecting digital components for multiple digital component slots are described. In one aspect, a method includes receiving, from a client device and by a first MPC computer of a group of MPC computers that collaborate to perform MPC computations, a composite request for digital components to display in multiple digital component slots of an electronic resource. The composite request includes first secret shares of data identifying user groups that include a user of the client device as a member. A determination is made, in collaboration with one or more second MPC computers, a first secret share of a value of each of multiple candidate parameters of a candidate expression for each digital component in a set of digital components.

A multi-party password book distribution method based on secret sharing, a sender uploads a password book to an interstellar file system through public key encryption, adopts secret sharing to secret split information obtained by splicing a public key and a Hash of an expected receiver, a receiver obtains a secret share through biological feature recognition technology, and obtains a sender public key through a judgment function to download an original password book from the interstellar file system. According to experimental results and security analysis, the scheme can guarantee high time efficiency and resist impersonation attacks, and guarantee the safe transmission of the password book. The present application solves the security problem of password book distribution by adopting a shamir threshold scheme, changes the threshold for safe password book distribution, dynamically solves the entry and exit of receivers, solves the problem that original covert communication can only perform one-to-one communication, and realizes one-to-many communication.

The invention discloses a homomorphic secret sharing method, which adopts a Shamir threshold secret sharing scheme, and can provide threshold security for the homomorphic secret sharing method, so that a server set or an access structure which does not meet a certain condition cannot recover a specific secret value from a given secret share; meanwhile, a Shamir threshold secret sharing scheme is combined with a homomorphic encryption algorithm, so that the degree of a polynomial of the homomorphic secret sharing scheme can be improved; a homomorphic encryption algorithm is adopted to perform encryption calculation on secret input, so that after the secret input is calculated by an unauthorized server, a calculation result can be decrypted by an output server to obtain a function value of the secret input under a given function.

This invention discloses a secure distributed backup method for databases based on secret sharing, relating to the field of database security technology. The method includes: selecting n backup nodes, setting a recovery threshold t, extracting sensitive information from the database, and using a secret sharing algorithm to divide the sensitive information into n shares within the backup nodes; allocating each share to a corresponding backup node for storage; adding a password share to each share stored in each backup node to construct an encrypted share; creating a mapping table based on the correspondence between backup nodes and encrypted shares; receiving a recovery request, retrieving the encrypted share from several backup nodes based on the response request and the mapping table, and decoding the retrieved encrypted share to obtain the sensitive information. This invention ensures data security during the backup process by dividing and encrypting sensitive information in the database and then distributing the divided information across multiple backup nodes.

A system and method for digitally signing data. A method includes generating, by a first device, at least one first secret share based on a secret key selected by the first device, wherein the first device is offline with respect to a second device; partially signing, by the first device, data using the at least one secret share, wherein the data is received from the second device without establishing direct communication between the first device and the second device; and sending the partially signed data from the first device to the second device, wherein the second device generates signed data using the partially signed data, wherein the signed data corresponds to a public key generated based on the at least one first secret share and at least one second secret share generated by the second device.

The application discloses a privacy protection neural network inference method based on multi-point evaluation. K Segment k Polynomial, and a safe multiplication tree and a safe remainder tree are established, and then a remainder polynomial is safely evaluated. Since the polynomial power to be evaluated in the online stage is reduced, the number of self-multiplication items is reduced, thereby reducing the communication round number of overall polynomial evaluation, reducing the number of calls of the safe multiplication triplets in the secret sharing, and correspondingly reducing the offline pre-computation cost. In addition, the layered structure of the safe multiplication tree and the remainder tree supports parallel computation of multi-point input, and can fully utilize the parallel computation capacity of a modern multi-core processor or a GPU.

The specification provides a tree model two-party joint reasoning method. A first party and a second party respectively hold different types of characteristics of a target object. The first party and the second party also jointly hold a tree model, in which the feature numbers of the split features of the non-leaf nodes are held by the non-feature party, and the split thresholds are held by the two parties in the form of secret sharing fragments. For each non-leaf node, according to the homomorphic operation between the homomorphic ciphertext of the non-leaf node feature number vector and the characteristic value of the target object, the fragment of the characteristic value of the split feature of the target object is obtained. And through the secret comparison jointly executed by the two parties, according to the split threshold fragment of the non-leaf node, the fragment of the secret comparison result is determined. The two parties jointly determine the fragment of the leaf node array according to the fragment of the secret comparison result of each non-leaf node. Finally, the two parties jointly determine the prediction result of the target object according to the leaf node array and the fragment of the prediction value of each leaf node.

This invention provides a method and system for secure FLAG generation and synchronization in a distributed environment, comprising: constructing a secret share generation algorithm and reconstruction mechanism based on the residual theorem to establish a distributed key management foundation; using attention networks and optimization algorithms to process multi-source random factors and generate a high-entropy random factor set; then designing a multi-dimensional representation space mapping and a distributed consensus protocol to coordinate FLAG generation among nodes and perform encrypted protection; and constructing a multi-layer caching architecture and synchronization protocol to realize FLAG storage, transmission, and verification. This invention achieves a highly secure and highly available distributed FLAG system.

The invention provides an anti-collusion low-overhead two-way verification privacy protection federated learning method, which specifically comprises the following steps: S1, a trusted third party initializes system parameters, and a server initializes a global model and distributes the global model to a client; s2, the client uses local data to train a global model and carries out compression encryption, signature and share division on an output calculation result; s3, the server reconstructs the client ciphertext by integrating the share shared by the client and verifies the correctness of the reconstruction result; s4, aggregating the ciphertext by the server; and S5, the client decrypts and decompresses the aggregated ciphertext and verifies an aggregation result. The invention provides an anti-collusion low-overhead two-way verification privacy protection federated learning method. According to the method, while bidirectional verification between the client and the server is realized, a scheme is constructed by using a super-incremental sequence and lightweight cryptographic primitive secret sharing, so that the computing load of the system is effectively reduced, and the tolerance of the system to collusion of the client and the server is improved.

A method for standard-compliant, confidential receipt of data is set forth. The method is executable via a system for data processing, in particular a system for standard-compliant, confidential receipt of data. The data is divided into secret shares already upon receipt, so that none of the servers involved can see the received data in plain text and the method thus enables encrypted data processing and secure data receipt.

This invention relates to the fields of federated learning and cryptography, specifically to a secure three-party aggregation method for federated learning in the Industrial Internet of Things (IIoT). The method includes an initialization phase, a training phase, an online phase, and a verification phase. By setting up an architecture with one honest server and two aggregation servers, each server receives a portion of the secret share of local model gradient parameters sent by various IoT devices, thus protecting the privacy of local data. The honest server assists the two aggregation servers in interactively executing a three-party weight calculation protocol to calculate the secret share of the aggregation weights. They also interactively execute a three-party multi-weight aggregation protocol to calculate the secret share of the gradient parameters of the current aggregation model. Furthermore, a linear homomorphic hashing method is used to verify the correctness of the gradient parameters of the current aggregation model. This method solves the problems of low efficiency, weak security, and low robustness in federated learning during model training and aggregation, and can promote data flow and utilization in IoT scenarios.

The invention relates to a novel quantum secret sharing method based on a Bell state. The method comprises the following steps that: firstly, a secret distributor sends a sequence of mixed Bell states and decoy particles to participants; secondly, the participants encode secret shares of the participants into corresponding Pauli operators, and the Pauli operators are applied to the received particle sequences; and finally, the participants recover the shared secret information through cooperation. And shared secret information can be obtained by executing Bell-based measurement by a secret distributor. Compared with other quantum secret sharing methods, the method has the advantages that the secret distributor does not grasp the secret share of the participant, even if the secret distributor grasp the shared secret information, the secret share of the participant cannot be deduced, and the privacy and benefits of the participant can be effectively protected. On the basis of meeting safety, a Bell state is used as a quantum carrier, and compared with an entangled state of n particles, the Bell state is easier to prepare; and meanwhile, various eavesdropping attacks can be effectively resisted. Compared with a traditional method, the method is more practical and higher in efficiency.

To provide a method for generating shares of a shared secret.SOLUTION: A computer-implemented method of generating shares of a shared secret, each of a group of participants having a respective first secret share of the shared secret, the method comprising the steps, performed by a first participant of the group, of generating a respective blinded share of the shared blinded secret, and obtaining at least a threshold number of respective intermediate shares from each of the first group of participants, wherein: Generating a respective intermediate share based on the respective blind share and the respective first secret share, generating an intermediate value based on each of the obtained intermediate shares, and generating a respective second secret share of the shared secret, wherein the respective second shared secret is generated based on the intermediate value and the respective blind share.SELECTED DRAWING: Figure 1

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for batch retrieving data are described. In one aspect, a method includes receiving, from a client device and by a first multi-party computation (MPC) server of a cluster of MPC servers, a batch request for retrieving multiple database values stored in one or more databases. The batch request includes a first byte array that includes, for each requested key of multiple requested keys, a first secret share of the requested key. Each database includes multiple data items that each include a database key and a corresponding value. The MPC server processes each database key to generate first secret shares of matching data indicating whether the database key matches at least one requested key. The MPC server generates one or more results that represent database values corresponding to each database key that matches at least one requested key.

Embodiments of the present specification provide a data processing method and device. The method is performed by any target party in multiple parties, comprising: first acquiring two table shards corresponding to two data tables, and a target table shard corresponding to any target table in the two data tables is one of multiple secret sharing shards of the target table; then, for the target table shard, based on a primary key column sub-shard contained in the target table shard and a corresponding generated key shard, a pseudo-random array sub-shard is determined for restoring a pseudo-random array; the target table shard is subjected to permutation processing based on the pseudo-random array to obtain a corresponding sorted table shard; and then, based on two sorted table shards corresponding to the two table shards, a connection table shard is determined, which is one of multiple secret sharing shards of a primary key connection table corresponding to the two data tables.

The invention discloses a security reasoning method and system for privacy protection machine learning, and relates to the technical field of privacy protection machine learning, and the method comprises the steps: calculating a secret share according to a model input matrix and sand secret sharing and random vector compression secret sharing of model parameters, reconstructing a secret value and packaging to obtain TP secret sharing of a model linear layer operation result; the method comprises the following steps of: performing mask calculation on an input matrix of a nonlinear layer in TP secret sharing by adopting a random number to obtain a mask random number, calculating TP secret sharing at a first different position in bit representation of the mask random number and a required mask random number so as to determine the highest bit of the input matrix of the nonlinear layer, calculating an RELU function, and calculating a truncation result according to the RELU function, and a nonlinear layer output result is obtained based on a truncation result and a random mask in TP secret sharing, so that the communication and calculation overhead of a security reasoning online stage is reduced.

This invention discloses a privacy-preserving method for federated learning based on adaptive quantization and sparse secret sharing: the server generates a globally random seed and broadcasts it to the clients. Each client locally generates a sparse neighbor set and negotiates a shared key, reducing communication complexity to logarithmic levels. After training gradients, the clients adaptively quantize to an integer ring, using their private seed and shared key to generate private masks and pairwise masks, which are then superimposed and uploaded. Simultaneously, the seed and key are split into secret shares and distributed to neighbors. The server directly sums these to obtain a preliminary aggregation result. After the window expires, it requests secret fragments to reconstruct the private seed and shared key, eliminating private masks and pairwise mask remnants caused by disconnected clients. After dequantization, the global model is updated. This invention reduces communication and computational overhead, eliminates the need for homomorphic encryption, has strong anti-disconnection capabilities, supports large-scale IoT devices, and achieves efficient and robust federated learning aggregation while ensuring information-theoretic security.

The invention relates to a KNN classification method, device, equipment and medium based on secret sharing privacy protection, and relates to a service party, a requesting party and three independent proxy computing parties without collusion, the method comprises the following steps: the service party and the requesting party respectively carry out secret sharing encryption on a reference data set and a new sample, and send the encrypted reference data set and new sample to the proxy computing parties; the proxy calculation party performs security calculation on the obtained data to obtain the distance from the new sample to each sample in the reference data set; in each round, the service party sends two sample serial numbers of which the distances need to be compared to the calculation agent party, and the agent calculation party calculates a distance difference value, processes the difference value and sends the difference value to the service party; and the service party performs sorting according to the encryption difference value of each round to obtain a distance-based sorting result of the samples in all the reference data sets so as to determine a KNN classification result of a new sample and return the KNN classification result to the requester. Compared with the prior art, the method has the advantages of good safety, high efficiency and the like.