NDLI: On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge

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On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge

Content Provider	ACM Digital Library
Author	Ramesh, Ganesh Lakshmanan, LAKS V. S. Ng, Raymond T.
Copyright Year	2008
Abstract	Decision makers of companies often face the dilemma of whether to release data for knowledge discovery, vis-a-vis the risk of disclosing proprietary or sensitive information. Among the various methods employed for “sanitizing” the data prior to disclosure, we focus in this article on anonymization, given its widespread use in practice. We do due diligence to the question “just how safe is the anonymized data?” We consider both those scenarios when the hacker has no information and, more realistically, when the hacker may have partial information about items in the domain. We conduct our analyses in the context of frequent set mining and address the safety question at two different levels: (i) how likely of being cracked (i.e., re-identified by a hacker), are the identities of individual items and (ii) how likely are sets of items cracked? For capturing the prior knowledge of the hacker, we propose a belief function, which amounts to an educated guess of the frequency of each item. For various classes of belief functions which correspond to different degrees of prior knowledge, we derive formulas for computing the expected number of cracks of single items and for itemsets, the probability of cracking the itemsets. While obtaining, exact values for more general situations is computationally hard, we propose a series of heuristics called the $\textit{O-estimates}.$ They are easy to compute and are shown fairly accurate, justified by empirical results on real benchmark datasets. Based on the O-estimates, we propose a recipe for the decision makers to resolve their dilemma. Our recipe operates at two different levels, depending on whether the data owner wants to reason in terms of single items or sets of items (or both). Finally, we present techniques for ascertaining a hacker's knowledge of correlation in terms of co-occurrence of items likely. This information regarding the hacker's knowledge can be incorporated into our framework of disclosure risk analysis and we present experimental results demonstrating how this knowledge affects the heuristic estimates we have developed.
Starting Page	1
Ending Page	44
Page Count	44
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/1409620.1409623
Volume Number	2
Issue Number	3
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2008-10-27
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Disclosure risk Anonymization Belief function Bipartite graphs Correlation Frequent itemsets Hacker Matching Prior knowledge Sampling
Content Type	Text
Resource Type	Article
Subject	Computer Science

Sl.	Authority	Responsibilities	Communication Details
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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8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge 1.

Perfect matchings via uniform sampling in regular bipartite graphs

Mining top-K frequent itemsets through progressive sampling

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

On the privacy of anonymized networks

A Min–Max Property of Chordal Bipartite Graphs with Applications

Half Sampling on Bipartite Graphs

Belief Propagation and LP Relaxation for Weighted Matching in General Graphs

To do or not to do: the dilemma of disclosing anonymized data (2005).

On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge

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On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge 1.

Perfect matchings via uniform sampling in regular bipartite graphs

Mining top-K frequent itemsets through progressive sampling

Efficient Discovery of Association Rules and Frequent Itemsets through Sampling with Tight Performance Guarantees

On the privacy of anonymized networks

A Min–Max Property of Chordal Bipartite Graphs with Applications

Half Sampling on Bipartite Graphs

Belief Propagation and LP Relaxation for Weighted Matching in General Graphs

To do or not to do: the dilemma of disclosing anonymized data (2005).

On disclosure risk analysis of anonymized itemsets in the presence of prior knowledge