38 results about "Data anonymity" patented technology

Techniques described herein provide data access and data anonymity management within various implementations of content distribution networks. In some embodiments, a data anonymity management system may include multiple different data stores configured to store identity data, user data, and / or linking data, as well as a data anonymity server configured to support data anonymity features such as active user data anonymity, one-way linking and permanently breakable links between user identity data and associated user data. In some embodiments, storage systems and techniques may be used for storing identity data, user data, and link data in multiple different data stores, with different data storage requirements and different processes and policies for data availability, data backup, online disaster recovery, offline storage archiving, and the like. Additionally, cryptographic hash functions may be used to implement one-way encryption between identity data and user data.

The invention concerns a data anonymity measuring device for selectively transmitting an anonymised database to a third party comprising: calculation means (402) coupled to at least one memory, the memory storing an original database (204) and said anonymized database (206), said calculation means arranged to calculate the conditional entropy for each entry in said anonymized database based on entries in said original database; comparing means (406) arranged to compare at least one of said conditional entropies with a threshold value; and output means (410) arranged to transmit said anonymized database based on the result of said comparison.

This application is applicable to the field of blockchain technology, and provides methods and devices for recruiting subjects, terminal equipment, and storage media. The method for recruiting subjects includes: acquiring first data sent by a recruiting platform, and comparing the first data with each of the second data in N pieces of second data stored in the blockchain to generate a duplicate check As a result, the duplicate check result is sent to the recruitment platform, and the recruitment platform determines whether the applicant meets the recruitment conditions according to the duplicate check result. Based on the decentralization of blockchain and the immutability of data, data can be shared among multiple recruitment platforms through blockchain to determine whether applicants meet the recruitment conditions, and to ensure that the data is authentic and valid. It effectively excludes professional subjects, and based on the data anonymity of the blockchain, multiple recruitment platforms cannot access each other's data, which can prevent information leakage and protect personnel privacy.

The present invention relates to systems and methods for anonymous and consistent data routing in a client-server architecture. Disclosed herein are systems and methods for sending user data in a client-server architecture with data anonymity and consistency. In an exemplary aspect, a client device may identify a structure to send to a server, where the structure includes user data. The client device may divide the structure into two or more substructures, and for each corresponding substructure in the two or more substructures, the client device may: (1) assign confidentiality to a corresponding a substructure; and (2) sending the corresponding substructure to the corresponding node based on the assigned confidentiality and the security degree of the corresponding node among the plurality of nodes. The respective nodes may be configured to apply the respective transformations to the respective substructures and to transmit the transformed respective substructures to the server. The server is configured to combine the received plurality of transformed substructures into a transformed structure.

The embodiment of this specification provides a data processing method for realizing privacy protection. The method includes: acquiring the sensor data to be processed, and the corresponding identity category label and service label, and the service label corresponds to the service prediction task for the user; then, the The sensor data is input into the data anonymous model to obtain anonymous data; further, on the one hand, the anonymous data is input into the pre-trained user identity recognition model to obtain the identity prediction result, which is used to determine the identity prediction loss in combination with the identity category label; On the one hand, the anonymous data is input into the pre-trained business prediction model to obtain the business prediction result, which is used to determine the business prediction loss in combination with the above business labels; then, the comprehensive loss is used to train the data anonymous model; the comprehensive loss is combined with the The identity prediction loss is negatively correlated and positively correlated with the business prediction loss; the trained data anonymity model is used to anonymize the target sensor data.

The invention discloses a MapReduce-based distributed data anonymity processing method, which comprises a server side and computer terminals, wherein an original data table is stored in the server side to carry out global generalization on data and give a generalized lattice which is likely to meet k- anonymity; the server side utilizes a method of bisection to allocate a computational node to each computer terminal; each computer terminal carries out computation in parallel and returns a value to the server side according to a computation condition; if the return value does not meet k- anonymity, the server side sends a descendant node determined by the method of bisection to each computer node, otherwise the server side sends an ancestor node determined by the method of bisection to a computer; and each computer terminal recalculates according to a new node given by the server side until all nodes which meet k- anonymity are found. The method solves the trouble between explosive data growth and existing server storage and computational capabilities, and the efficiency of massive data processing is improved.