NDLI: Information Measures in Statistical Privacy and Data Processing Applications

Content Provider	ACM Digital Library
Author	Lin, Bing-Rong Kifer, Daniel
Copyright Year	2015
Abstract	In statistical privacy, $\textit{utility}$ refers to two concepts: information preservation, how much statistical information is retained by a sanitizing algorithm, and $\textit{usability,}$ how (and with how much difficulty) one extracts this information to build statistical models, answer queries, and so forth. Some scenarios incentivize a separation between information preservation and usability, so that the data owner first chooses a sanitizing algorithm to maximize a measure of information preservation, and, afterward, the data consumers process the sanitized output according to their various individual needs [Ghosh et al. 2009; Williams and McSherry 2010]. We analyze the information-preserving properties of utility measures with a combination of two new and three existing utility axioms and study how violations of an axiom can be fixed. We show that the average (over possible outputs of the sanitizer) error of Bayesian decision makers forms the unique class of utility measures that satisfy all of the axioms. The axioms are agnostic to Bayesian concepts such as subjective probabilities and hence strengthen support for Bayesian views in privacy research. In particular, this result connects information preservation to aspects of usability—if the information preservation of a sanitizing algorithm should be measured as the average error of a Bayesian decision maker, shouldn’t Bayesian decision theory be a good choice when it comes to using the sanitized outputs for various purposes? We put this idea to the test in the unattributed histogram problem where our decision-theoretic postprocessing algorithm empirically outperforms previously proposed approaches.
Starting Page	1
Ending Page	29
Page Count	29
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2700407
Volume Number	9
Issue Number	4
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Privacy Decision theory Differential privacy Information measures Minimax Utility
Content Type	Text
Resource Type	Article
Subject	Computer Science

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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