The Laplace Mechanism has optimal utility for differential privacy over continuous queries
Differential Privacy protects individuals' data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the query results makes the released information less specific but, unavoidably, also decreases its utility. Yet it has been shown tha...
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| Vydáno v: | Proceedings of the 36th Annual ACM/IEEE Symposium on Logic in Computer Science s. 1 - 12 |
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IEEE
29.06.2021
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| Abstract | Differential Privacy protects individuals' data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the query results makes the released information less specific but, unavoidably, also decreases its utility. Yet it has been shown that for discrete data (e.g. counting queries), a mandated degree of privacy and a reasonable interpretation of loss of utility, the Geometric obfuscating mechanism is optimal: it loses as little utility as possible [Ghosh et al. [1]].For continuous query results however (e.g. real numbers) the optimality result does not hold. Our contribution here is to show that optimality is regained by using the Laplace mechanism for the obfuscation.The technical apparatus involved includes the earlier discrete result [Ghosh op. cit.], recent work on abstract channels and their geometric representation as hyper-distributions [Alvim et al. [2]], and the dual interpretations of distance between distributions provided by the Kantorovich-Rubinstein Theorem. |
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| AbstractList | Differential Privacy protects individuals' data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the query results makes the released information less specific but, unavoidably, also decreases its utility. Yet it has been shown that for discrete data (e.g. counting queries), a mandated degree of privacy and a reasonable interpretation of loss of utility, the Geometric obfuscating mechanism is optimal: it loses as little utility as possible [Ghosh et al. [1]].For continuous query results however (e.g. real numbers) the optimality result does not hold. Our contribution here is to show that optimality is regained by using the Laplace mechanism for the obfuscation.The technical apparatus involved includes the earlier discrete result [Ghosh op. cit.], recent work on abstract channels and their geometric representation as hyper-distributions [Alvim et al. [2]], and the dual interpretations of distance between distributions provided by the Kantorovich-Rubinstein Theorem. |
| Author | McIver, Annabelle Fernandes, Natasha Morgan, Carroll |
| Author_xml | – sequence: 1 givenname: Natasha surname: Fernandes fullname: Fernandes, Natasha organization: Macquarie University,Department of Computing,Sydney – sequence: 2 givenname: Annabelle surname: McIver fullname: McIver, Annabelle organization: Macquarie University,Department of Computing,Sydney – sequence: 3 givenname: Carroll surname: Morgan fullname: Morgan, Carroll organization: UNSW,School of Computer Science and Engineering,Sydney |
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| Snippet | Differential Privacy protects individuals' data when statistical queries are published from aggregated databases: applying "obfuscating" mechanisms to the... |
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| SubjectTerms | abstract channels Computer science Differential privacy hyper-distributions Laplace mechanism optimal mechanisms quantitative information flow utility |
| Title | The Laplace Mechanism has optimal utility for differential privacy over continuous queries |
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