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CMU-ISRI-04-120
Institute for Software Research International
School of Computer Science, Carnegie Mellon University
CMU-ISRI-04-120
Secure Computation of k-Anonymous Distributed Data
Bradley Malin
May 2004
Data Privacy Laboratory
CMU-ISRI-04-120.ps
CMU-ISRI-04-120.pdf
Keywords: Privacy, confidentiality, security, re-identification,
k-anonymity, multiparty computation, quasi-commutative encryption,
privacy protocols
In a distributed environment, such as the World Wide Web, an
individual leaves behind personal data at many different locations.
To protect the privacy of an individual's sensitive information,
locations make separate releases of identifiable data (e.g. name
or social security number), and sensitive data (e.g. visitor's
IP address). To the releasing location the data appears unlinkable,
however, links can be established when multiple locations releases
are available. This problem, known as trail re-identification,
manifests when an individual's location-visit patterns are
reconstructed from, and linked between, sensitive and identifiable
releases. In this paper, we present a protocol that enables
locations to prevent trail re-identification without revealing
identified or sensitive data. Instead, locations communicate
encrypted versions of their datasets, such that decrypted data
is never revealed until completion of the protocol. Via the
protocol, every piece of sensitive data, released from any set
of locations, is guaranteed to be equally relatable to at least
k identities, or is k-anonymous.
16 pages
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