13 results about "Privacy protection" patented technology

The invention provides a track privacy protection algorithm based on angle division and position semantics. The track privacy protection algorithm comprises the steps of 1, conducting initializing to generate a grid map model and randomly distributing all position types for the grid map model; 2, inputting information by a user; 3, processing the information input by the user; 4, providing a query request by the user and inputting a position coordinate of a current moment; 5, conducting angle division according to the position coordinate to generate many false position Sudoku areas; 6, selecting an appropriate false position according to current position semantics; 7, making the false positions generated at all the moments connected to generate a false track. According to the track privacy protection algorithm based on the angle division and position semantics, the position semantics and the track privacy protection algorithm are combined, the degree of popularization of positions, the sensitivity and the degree of privacy are utilized to quantify position semantic information, mathematical modeling is conducted on the position semantic information, the position semantic information is accurately described through a model, and privacy protection of a real track of the user can be achieved.

The embodiment of the invention provides a method and device for training a graph neural network based on privacy protection, and the method comprises the steps: firstly obtaining an original relationnetwork graph, wherein any first node in the graph has a corresponding neighbor node set; and for any second node in the neighbor node set, inputting the node information of the second node, the nodeinformation of the first node and the connection information of the second node and the first node into the multi-layer neural network to obtain the matching degree of the second node and the first node; then, according to the matching degree corresponding to each neighbor node in the neighbor node set, sampling the neighbor node set to obtain a sampling neighbor node set of the first node; secondly, forming a sparse relation network graph based on the sampling neighbor node sets corresponding to the nodes in the original graph; then, based on the sparse relationship network graph, training agraph neural network.

The invention discloses a data acquisition and analysis system based on a wireless body area network and a low-energy-consumption data fusion privacy protection algorithm. The system comprises: a dataacquisition node which is electrically connected with data monitoring equipment and is used for receiving data of the monitoring equipment; a data transmission node which is electrically connected with the data acquisition node and is used for carrying out noise filtering and first-level data fusion calculation; and a data analysis node which is in signal connection with the data transmission node and is used for carrying out final data fusion analysis calculation. A data fusion model of a tree structure is used, on the basis of a data fusion function structure expression, a data fusion function based on unequal time intervals is provided, and a random key distribution strategy is adopted for data encryption and decryption; the method has the characteristics of small data communication traffic, high privacy protection, high accuracy and low energy consumption.

A traffic sensor includes an imaging unit that generates a recognition record by image recognition processing a captured image of a vehicle. The recognition record includes personal-identification data useable to identify the vehicle owner, and additional data not useable to identify the vehicle owner. The traffic sensor also includes an obfuscation unit that generates an obfuscated record from the recognition record by converting the personal-identification data into a unique-identifier. The obfuscated record includes the unique-identifier and the additional data. The traffic sensor further includes a transceiver configured to transmit the obfuscated record to a first off-site server without transmitting the recognition record to the first off-site server.

The invention provides a privacy protection method and system for fingerprint authentication, and the method comprises the steps: decomposing fingerprint data through employing singular matrix decomposition of low-rank matrix approximation, obtaining a low-rank singular value matrix, and approximately representing a fingerprint matrix; calculating an optimal rank by using an exponential mechanism; then, through truncated singular value matrix decomposition, obtaining a low-rank expression under an optimal rank of the fingerprint matrix; adding Laplace noise to each singular value of the singular value matrix to obtain a disturbance singular value matrix; performing post refinement on the disturbance singular value by using constraint reasoning, and rearranging a singular value sequence to obtain a post-processed singular value matrix; and finally, reconstructing a disturbance fingerprint according to the post-processed singular value matrix. Compared with a similar existing method, the fingerprint privacy protection method has the advantages that fingerprint privacy protection is achieved with minimum information disturbance, irreversibility, non-linkability and accurate identity authentication of fingerprints can be guaranteed more efficiently, and the method has high practicability.

The invention provides a differential privacy release method and system for associated tuple data, and relates to the field of data mining and privacy protection, which comprises the following steps of: generating initialization noise and disturbing an initialization query result, generating noise meeting a specific auto-covariance matrix according to a new query function, disturbing the query result, finally, processing all queries by adopting an iterative mechanism until the query function sequence is processed, and outputting and publishing a disturbance query result. According to the generalized Laplace noise generation method and the practical and efficient iteration and updating mechanism provided by the invention, the problem that the privacy protection strength is reduced due to the fact that noise generated by differential privacy is independent when tuple data to be protected is related in an existing publishing method of utilizing differential privacy protection associated tuple data is solved.

The invention discloses a privacy protection-based alliance learning system and method for realizing whole genome association analysis, and the system comprises a global server which is connected with an original data terminal and an authorization terminal, wherein the global server comprises a data preprocessing module, a main program module, and a request handle module. The main program module comprises an alliance type principal component analysis module and an alliance type generalized linear model; the method comprises the following steps: 1, authorizing a researcher, creating an alliance type whole-genome correlation analysis research instance, and submitting the alliance type whole-genome correlation analysis research instance to a global server; 2, enabling each data owner and the global server to cooperatively execute data preprocessing to obtain preprocessed data; 3, through the preprocessed data, enabling the global server to execute an alliance type principal component analysis method and then execute an alliance type generalized linear model to generate an analysis result report; 4, enabling the global server to send the final result report to the authorized researchers.