Access the full text.
Sign up today, get DeepDyve free for 14 days.
Loading next page...
/lp/association-for-computing-machinery/randomization-in-privacy-preserving-data-mining-YINBzmAMeK
References (29)
-
J. Kleinberg, C. Papadimitriou, P. Raghavan (2001)
On the value of private information -
L. Breiman, J. Friedman, R. Olshen, C. Stone (1984)
Classification and Regression TreesBiometrics, 40
-
Benny Pinkas (2002)
Cryptographic techniques for privacy-preserving data miningSIGKDD Explor., 4
-
S. Fienberg, U. Makov, R. Steele (1998)
Disclosure limitation using perturbation and related methods for categorical data -
A. Dempster, N. Laird, D. Rubin (1977)
Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper -
J. Gouweleeuw, P. Kooiman, L. Willenborg, P. De-wolf (1997)
Post randomisation for statistical disclosure control: Theory and implementation -
G. Duncan, R. Krishnan, R. Padman (1997)
Cell suppression to limit content-based disclosureProceedings of the Thirtieth Hawaii International Conference on System Sciences, 3
-
R. Agrawal, R. Srikant (1994)
Fast Algorithms for Mining Association Rules in Large Databases -
G. Duncan, Sumitra Mukherjee (2000)
Optimal Disclosure Limitation Strategy in Statistical Databases: Deterring Tracker Attacks through Additive NoiseJournal of the American Statistical Association, 95
-
D. Lambert (1993)
Measures of Disclosure Risks and Harm -
(1996)
New directions in disclosure limitation at the census bureau -
V. Iyengar (2002)
Transforming data to satisfy privacy constraintsProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
-
P. Samarati, L. Sweeney (1998)
Generalizing data to provide anonymity when disclosing information (abstract) -
Chris Clifton, Murat Kantarcioglu, Jaideep Vaidya, Xiaodong Lin, M. Zhu (2002)
Tools for privacy preserving distributed data miningSIGKDD Explor., 4
-
A. Evfimievski, R. Srikant, R. Agrawal, J. Gehrke (2002)
Privacy preserving mining of association rulesProceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
-
P.J Cameron, H.D Macpherson (1985)
Rank three permutation groups with rank three subconstituentsJ. Comb. Theory, Ser. B, 39
-
(1993)
Statistical analysis of masked data -
P. Samarati, L. Sweeney (1998)
Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression -
G. McLachlan, T. Krishnan (1996)
The EM algorithm and extensions -
S. Rizvi, J. Haritsa (2002)
Maintaining Data Privacy in Association Rule Mining -
R. Agrawal, R. Srikant (2000)
Privacy-preserving data mining -
T. Evans, L. Zayatz, John Slanta (1998)
Using noise for disclosure limi-tation of establishment tabular data -
R. Agrawal, R. Srikant (1998)
Fast Algorithms for Mining Association Rules -
R. Agrawal, T. Imielinski, A. Swami (1993)
Mining association rules between sets of items in large databasesACM SIGMOD Record, 22
-
C. Shannon (1949)
Communication theory of secrecy systemsBell Syst. Tech. J., 28
-
Sumitra Mukherjee, G. Duncan (1997)
Disclosure limitation through additive noise data masking: analysis of skewed sensitive dataProceedings of the Thirtieth Hawaii International Conference on System Sciences, 3
-
D. Agrawal, C. Aggarwal (2001)
On the design and quantification of privacy preserving data mining algorithms -
Jay-J. Kim, W. Winkler (1995)
MASKING MICRODATA FILES -
SIGKDD Explorations
- Publisher
- Association for Computing Machinery
- Copyright
- Copyright © 2002 by ACM Inc.
- ISSN
- 1931-0145
- DOI
- 10.1145/772862.772869
- Publisher site
- See Article on Publisher Site
Abstract
Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this information. The clients can protect privacy of their data by perturbing it with a randomization algorithm and then submitting the randomized version. The randomization algorithm is chosen so that aggregate properties of the data can be recovered with sufficient precision, while individual entries are significantly distorted. How much distortion is needed to protect privacy can be determined using a privacy measure. Several possible privacy measures are known; finding the best measure is an open question. This paper presents some methods and results in randomization for numerical and categorical data, and discusses the issue of measuring privacy.
Journal
ACM SIGKDD Explorations Newsletter – Association for Computing Machinery
Published: Dec 1, 2002