2017

One-sided Differential Privacy

Doudalis, Stelios, Kotsogiannis, Ios, Haney, Samuel et al.

Understand

In this paper, we study the problem of privacy-preserving data sharing, wherein only a subset of the records in a database are sensitive, possibly based on predefined privacy policies.

Existing solutions, viz, differential privacy (DP), are over-pessimistic and treat all information as sensitive.
Alternatively, techniques, like access control and personalized differential privacy, reveal all non-sensitive records truthfully, and they indirectly leak information about sensitive records through exclusion attacks.
Motivated by the limitations of prior work, we introduce the notion of one-sided differential privacy (OSDP).

Built on

Role-based access control models
R. S. Sandhu, E. J. Coyne, H. L. Feinstein, and C. E. Youman · 1996
Earlier work this paper cites.
Privacy in e-commerce: Examining user scenarios and privacy preferences
M. S. Ackerman, L. F. Cranor, and J. Reagle · 1999
Earlier work this paper cites.
A flexible authorization mechanism for relational data management systems
E. Bertino, S. Jajodia, and P. Samarati · 1999
Earlier work this paper cites.
Consumer privacy and name removal across direct marketing channels: Exploring opt-in and opt-out alternatives
G. R. Milne and A. J. Rohm · 2000
Earlier work this paper cites.
The virtual private database in oracle9ir2
K. Browder and M. Davidson · 2002
Earlier work this paper cites.
A formal analysis of information disclosure in data exchange
G. Miklau and D. Suciu · 2004
Earlier work this paper cites.
Extending query rewriting techniques for fine-grained access control
S. Rizvi, A. Mendelzon, S. Sudarshan, and P. Roy · 2004
Earlier work this paper cites.
Privacy and rationality in individual decision making
A. Acquisti and J. Grossklags · 2005
Earlier work this paper cites.
Privacy in e-commerce: Stated preferences vs. actual behavior
B. Berendt, O. Günther, and S. Spiekermann · 2005
Earlier work this paper cites.
Simulatable auditing
K. Kenthapadi, N. Mishra, and K. Nissim · 2005
Earlier work this paper cites.
A face is exposed for aol searcher no. 4417749
M. Barbaro and T. Z. Jr · 2006
Earlier work this paper cites.
Calibrating noise to sensitivity in private data analysis
C. Dwork, F. McSherry, K. Nissim, and A. Smith · 2006
Earlier work this paper cites.

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