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A Paradigm for Masking (Camouflaging) Information
Author(s) -
KellerMcNulty Sallie,
Nakhleh Charles W.,
Singpurwalla Nozer D.
Publication year - 2005
Publication title -
international statistical review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.051
H-Index - 54
eISSN - 1751-5823
pISSN - 0306-7734
DOI - 10.1111/j.1751-5823.2005.tb00152.x
Subject(s) - premise , computer science , entropy (arrow of time) , information theory , kullback–leibler divergence , confidentiality , masking (illustration) , constraint (computer aided design) , data mining , computer security , artificial intelligence , mathematics , statistics , art , philosophy , linguistics , physics , geometry , quantum mechanics , visual arts
Summary This is an expository paper. Here we propose a decision‐theoretic framework for addressing aspects of the confidentiality of information problems in publicly released data. Our basic premise is that the problem needs to be conceptualized by looking at the actions of three agents: a data collector, a legitimate data user, and an intruder. Here we aim to prescribe the actions of the first agent who desires to provide useful information to the second agent, but must protect against possible misuse by the third. The first agent is under the constraint that the released data has to be public to all; this in some societies may not be the case.A novel aspect of our paper is that all utilities—fundamental to decision making—are in terms of Shannon's information entropy. Thus what gets released is a distribution whose entropy maximizes the expected utility of the first agent. This means that the distribution that gets released will be different from that which generates the collected data. The discrepancy between the two distributions can be assessed via the Kullback–Leibler cross‐entropy function. Our proposed strategy therefore boils down to the notion that it is the information content of the data, not the actual data, that gets masked. Current practice of “statistical disclosure limitation” masks the observed data via transformations or cell suppression. These transformations are guided by balancing what are known as “disclosure risks” and “data utility”. The entropy indexed utility functions we propose are isomorphic to the above two entities. Thus our approach provides a formal link to that which is currently practiced in statistical disclosure limitation.