DocumentCode :
1048159
Title :
Privacy: a machine learning view
Author :
Vinterbo, Staal A.
Author_Institution :
Decision Syst. Group, Brigham & Women´´s Hosp., Boston, MA, USA
Volume :
16
Issue :
8
fYear :
2004
Firstpage :
939
Lastpage :
948
Abstract :
The problem of disseminating a data set for machine learning while controlling the disclosure of data source identity is described using a commuting diagram of functions. This formalization is used to present and analyze an optimization problem balancing privacy and data utility requirements. The analysis points to the application of a generalization mechanism for maintaining privacy in view of machine learning needs. We present new proofs of NP-hardness of the problem of minimizing information loss while satisfying a set of privacy requirements, both with and without the addition of a particular uniform coding requirement. As an initial analysis of the approximation properties of the problem, we show that the cell suppression problem with a constant number of attributes can be approximated within a constant. As a side effect, proofs of NP-hardness of the minimum k-union, maximum k-intersection, and parallel versions of these are presented. Bounded versions of these problems are also shown to be approximable within a constant.
Keywords :
computational complexity; data privacy; generalisation (artificial intelligence); inference mechanisms; learning (artificial intelligence); optimisation; NP-hardness; cell suppression problem; data set; data source identity; data utility requirements; generalization mechanism; machine learning; optimization problem; Aggregates; Control systems; Data privacy; Database systems; Helium; Humans; Insurance; Machine learning; Protection; US Government; 65; Privacy; approximation properties; combinatorial optimization; complexity; disclosure control; machine learning.;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2004.31
Filename :
1318579
Link To Document :
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