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Privlava : synthesizing relational data with foreign keys under differential privacy
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Cai, Kuntai, Xiao, Xiaokui and Cormode, Graham (2023) Privlava : synthesizing relational data with foreign keys under differential privacy. In: ACM SIGMOD/PODS International Conference on Management of Data, Seattle, WA, USA, 18-23 Jun 2023 (In Press)
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Abstract
Answering database queries while preserving privacy is an important problem that has attracted considerable research attention in recent years. A canonical approach to this problem is to use synthetic data. That is, we replace the input database R with a synthetic database R∗ that preserves the characteristics of R, and use R∗ to answer queries. Existing solutions for relational data synthesis, however, either fail to provide strong privacy protection, or assume that R contains a single relation. In addition, it is challenging to extend the existing single-relation solutions to the case of multiple relations, because they are unable to model the complex correlations induced by the foreign keys. Therefore, multi-relational data synthesis with strong privacy guarantees is an open problem. In this paper, we address the above open problem by proposing PrivLava, the first solution for synthesizing relational data with foreign keys under differential privacy, a rigorous privacy framework widely adopted in both academia and industry. The key idea of PrivLava is to model the data distribution in R using graphical models, with latent variables included to capture the inter-relational correlations caused by foreign keys. We show that PrivLava supports arbitrary foreign key references that form a directed acyclic graph, and is able to tackle the common case when R contains a mixture of public and private relations. Extensive experiments on census data sets and the TPC-H benchmark demonstrate that PrivLava significantly outperforms its competitors in terms of the accuracy of aggregate queries processed on the synthetic data.
Item Type: | Conference Item (Paper) | |||||||||||||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | |||||||||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Data protection, Privacy, Right of, Computer security, Relational databases | |||||||||||||||
Official Date: | 2023 | |||||||||||||||
Dates: |
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Status: | Peer Reviewed | |||||||||||||||
Publication Status: | In Press | |||||||||||||||
Reuse Statement (publisher, data, author rights): | Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore. © {Authors/ACM} 2023. This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM SIGMOD/PODS International Conference on Management of Data, http://dx.doi.org/10.1145/{number}. | |||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||
Date of first compliant deposit: | 5 May 2023 | |||||||||||||||
Date of first compliant Open Access: | 11 May 2023 | |||||||||||||||
RIOXX Funder/Project Grant: |
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Conference Paper Type: | Paper | |||||||||||||||
Title of Event: | ACM SIGMOD/PODS International Conference on Management of Data | |||||||||||||||
Type of Event: | Conference | |||||||||||||||
Location of Event: | Seattle, WA, USA | |||||||||||||||
Date(s) of Event: | 18-23 Jun 2023 | |||||||||||||||
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Open Access Version: |
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