Abstract
Differential privacy is the state-of-the-art for preserving privacy and differential privacy mechanism based on Laplace distribution with mean 0 is common practice. However, privacy budget is exhausted so quick that the number of queries is not enough. In this paper, a differential privacy mechanism is proposed to optimize the number of queries for application scenario of multiple users. We isolate different users by assigning various noise distribution with non-zero mean to different users. First, in terms of privacy guarantee, the proposed mechanism is better than common practice. Second, for the utility aspect, the accuracy of proposed mechanism is analyzed from the view of data distribution’s distortion and the view of noise’s absolute value.
Highlights
Differential privacy is the state-of-the-art method for preserving privacy
In order to tackle the problem of excessive privacy budget, a multiple users mechanism is proposed, and the contributions include: (1) We propose a multiple users differential privacy mechanism whose privacy budget does not increase when number of users increases
The rij = rij + rij is the noisy answer of query qij over database D, where the rij is the true answer of the qij and rij is noise drawn from Laplace distribution with scale parameter b = k D and the location parameter u = ui
Summary
Differential privacy is the state-of-the-art method for preserving privacy. It is based on good mathematics foundation and it can quantitatively describe the problem of privacy disclose. In order to tackle the problem of excessive privacy budget, a multiple users mechanism is proposed, and the contributions include:. (1) We propose a multiple users differential privacy mechanism whose privacy budget does not increase when number of users increases. Users are isolated by Laplace distribution with different non-zero mean so that the privacy budget is not exhausted so quick. There are papers focusing on privacy protection such as [3] and there are lots of papers focusing on privacypreserving data minting such as [1], [4], [5], [8], [10], [11] All of these papers are based on noise drawn from Laplace distribution with mean 0. Experiments are performed to verify our claims
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