Computational Disclosure Control - A Primer on Data Privacy Protection

Computational Disclosure Control - A Primer on Data Privacy Protection (2001)

Cached

Download Links

by Latanya Sweeney

Venue:	Massachusetts Institute of Technology
Citations:	23 - 2 self

Versions

Version History

Metadata Version 3

User correction supplied by SystemCorrections

Datum	Value	Source
TITLE	Computational Disclosure Control - A Primer on Data Privacy Protection	user correction - Legacy Corrections
AUTHOR NAME	Latanya Sweeney	SVM HeaderParse 0.1
AUTHOR AFFIL	Computational Disclosure Control 01/08/01 8:22 AM	SVM HeaderParse 0.2
ABSTRACT	Today's globally networked society places great demand on the dissemination and sharing of person-specific data for many new and exciting uses. Even situations where aggregate statistical information was once the reporting norm now rely heavily on the transfer of microscopically detailed transaction and encounter information. This happens at a time when more and more historically public information is also electronically available. When these data are linked together, they provide an electronic shadow of a person or organization that is as identifying and personal as a fingerprint even when the information contains no explicit identifiers, such as name and phone number. Other distinctive data, such as birth date and ZIP code, often combine uniquely and can be linked to publicly available information to re-identify individuals. Producing anonymous data that remains specific enough to be useful is often a very difficult task and practice today tends to either incorrectly believe confidentiality is maintained when it is not or produces data that are practically useless. The goal of the work presented in this book is to explore computational techniques for releasing useful information in such a way that the identity of any individual or entity contained in data cannot be recognized while the data remain practically useful. I begin by demonstrating ways to learn information about entities from publicly available information. I then provide a formal framework for reasoning about disclosure control and the ability to infer the identities of entities contained within the data. I formally define and present null-map, k-map and wrong-map as models of protection. Each model provides protection by ensuring that released information maps to no, k or incorrect entities, respectively. The book ends by examining four computational systems that attempt to maintain privacy while releasing electronic information. These systems are: (1) my Scrub System, which locates personally-identifying information in letters between doctors and notes written by clinicians; (2) my Datafly II System, which generalizes and suppresses values in field-structured data sets; (3) Statistics Netherlands' m-Argus System, which is becoming a European standard for producing public-use data; and, (4) my k-Similar algorithm, which finds optimal solutions such that data are minimally distorted while still providing adequate protection. By introducing anonymity and quality metrics, I show that Datafly II can overprotect data, Scrub and m-Argus can fail to provide adequate protection, but k-similar finds optimal results.	user correction - Legacy Corrections
YEAR	2001	INFERENCE
VENUE	Massachusetts Institute of Technology	INFERENCE
VENUE TYPE	TECHREPORT	INFERENCE
TECH	PhD thesis	INFERENCE
CITATIONS	62 found	ParsCit 1.0