Full Citation

Title: CALM: Consistent Adaptive Local Marginal for Marginal Release under Local Differential Privacy

Citation Type: Conference Paper

Publication Year: 2018

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Abstract: Marginal tables are the workhorse of capturing the correlations among a set of attributes. We consider the problem of constructing marginal tables given a set of user's multi-dimensional data while satisfying Local Differential Privacy (LDP), a privacy notion that protects individual user's privacy without relying on a trusted third party. Existing works on this problem perform poorly in the high-dimensional setting; even worse, some incur very expensive computational overhead. In this paper, we propose CALM, Consistent Adaptive Local Marginal, that takes advantage of the careful challenge analysis and performs consistently better than existing methods. More importantly, CALM can scale well with large data dimensions and marginal sizes. We conduct extensive experiments on several real world datasets. Experimental results demonstrate the effectiveness and efficiency of CALM over existing methods.

Url: https://dl.acm.org/citation.cfm?id=3243742

User Submitted?: No

Authors: Zhang, Zhikun; Wang, Tianhao; Li, Ninghui; He, Shibo; Chen, Jiming

Conference Name: ACM SIGSAC Conference on Computer and Communications Security

Publisher Location: Toronto, Canada

Data Collections: IPUMS USA

Topics: Methodology and Data Collection, Other

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