2012 DifferentialIdentifiability

• (Lee & Clifton, 2012) ⇒ Jaewoo Lee, and Chris Clifton. (2012). “Differential Identifiability.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339695

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Differential+Identifiability
• http://dl.acm.org/citation.cfm?id=2339530.2339695&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; differential privacy; identifiability; privacy

Abstract

A key challenge in privacy-preserving data mining is ensuring that a data mining result does not inherently violate privacy. ε-Differential Privacy appears to provide a solution to this problem. However, there are no clear guidelines on how to set ε to satisfy a privacy policy. We give an alternate formulation, Differential Identifiability, parameterized by the probability of individual identification. This provides the strong privacy guarantees of differential privacy, while letting policy makers set parameters based on the established privacy concept of individual identifiability.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 DifferentialIdentifiability	Jaewoo Lee Chris Clifton			Differential Identifiability				10.1145/2339530.2339695		2012