Abstract
This paper investigates the relation between three different notions of privacy: identifiability, differential privacy and mutual-information privacy. Under a privacy-distortion framework, where the distortion is defined to be the expected Hamming distance between the input and output databases, we establish some fundamental connections between these three privacy notions. Given a maximum distortion D, let ε∗ i(D) denote the smallest (best) identifiability level, and ε∗ d(D) the smallest differential privacy level. Then we characterize ε∗ i(D) and ε∗ d(D), and prove that ε∗ i(D) - εx ≤ ε∗ d(D) ≤ εε∗ i(D) for D in some range, where εx is a constant depending on the distribution of the original database X, and diminishes to zero when the distribution of X is uniform. Furthermore, we show that identifiability and mutual-information privacy are consistent in the sense that given a maximum distortion D in some range, there is a mechanism that optimizes the identifiability level and also achieves the best mutual-information privacy.
Original language | English (US) |
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Title of host publication | 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 1086-1092 |
Number of pages | 7 |
ISBN (Print) | 9781479980093 |
DOIs | |
State | Published - Jan 30 2015 |
Event | 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 - Monticello, United States Duration: Sep 30 2014 → Oct 3 2014 |
Other
Other | 2014 52nd Annual Allerton Conference on Communication, Control, and Computing, Allerton 2014 |
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Country/Territory | United States |
City | Monticello |
Period | 9/30/14 → 10/3/14 |
ASJC Scopus subject areas
- Computer Networks and Communications
- Computer Science Applications