Batched linear inquiry method based on difference privacy

Abstract

The invention discloses a batched linear inquiry method based on difference privacy. The method includes the following steps that 1, an original data set R is inquired, so that a data inquiry result set M is obtained; 2, attribute frequentness of the R is arrayed according to a descending order, attributes with the frequentness smaller than or equal to the minimum support are sieved, the attributes and data corresponding to the attributes are abandoned, data independence processing is performed on attributes larger than the minimum support, and an independent data set D larger than the minimumsupport in attribute frequentness is obtained; 3, the M is utilized for establishing a data independent load matrix W on the basis that an initial load matrix is established, and decomposed in parallel by the adoption of a parallel gradient descent matrix decomposition technique, and therefore a complete first matrix B of the decomposing result and a second matrix L of the decomposing result areobtained; 4, based on difference privacy, self-adaptation noise adding is performed, Lapras noise is added to the L and D, the abandoned attributes and data are restored, and a noise adding inquiry result data set S is obtained; 5, the S is returned to a user.

Description

Technical field [0001] The invention relates to the field of computer technology, in particular to a batch linear query method based on differential privacy. Background technique [0002] With the development of the Internet, mankind has entered the era of big data. When processing big data, batch linear query is the most commonly used operation, but its query scale is huge, the query process is complicated, and the performance is low. In addition, in the process of using big data, a lot of sensitive information is easily leaked, and query accuracy (data availability) and privacy protection cannot be guaranteed at the same time. [0003] The prior art algorithms cannot simultaneously guarantee the algorithm performance, query accuracy, and privacy protection degree of batch linear queries. In terms of algorithm performance, existing algorithms have high complexity and are not suitable for large-scale batch linear queries; in terms of query accuracy, existing algorithms add noise ...

AI Technical Summary

Problems solved by technology

When processing big data, batch linear query is the most commonly used operation, but its query scale is huge, the query process is complicated, and the performance is low

In addition, in the process of using big data, a lot of sensitive information is easily leaked, and it is impossible to guarantee query accuracy (data availability) and privacy protection at the same time.

[0003] Algorithms in the prior art cannot guarantee the algorithm performance, query accuracy and privacy protection degree of batch linear query at the same time

In terms of algorithm performance, the existing algorithm has high complexity and is not suitable for large-scale batch linear query; in terms of query accuracy, the existing algorithm adds noise to the query results to reduce the amount of noise required to optimize query accuracy

However, when the query sequence is arbitrarily given by the user, these mechanisms require a very large computational overhead in order to find the optimal noise distribution, which increases exponentially with the increase of the data dimension, and cannot be used for large data sets; in terms of privacy protection, existing The algorithm does not consider the amount of added noise combined with user permissions, and cannot guarantee that the amount of noise added to users with different permissions is appropriate. For users with high permissions, if too much noise is added, they will be greatly disturbed by noise and the query accuracy will decrease; For low-privilege users, adding too little noise will result in insufficient privacy protection

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