8,944 research outputs found
Data degradation to enhance privacy for the Ambient Intelligence
Increasing research in ubiquitous computing techniques towards the development of an Ambient Intelligence raises issues regarding privacy. To gain the required data needed to enable application in this Ambient Intelligence to offer smart services to users, sensors will monitor users' behavior to fill personal context histories. Those context histories will be stored on database/information systems which we consider as honest: they can be trusted now, but might be subject to attacks in the future. Making this assumption implies that protecting context histories by means of access control might be not enough. To reduce the impact of possible attacks, we propose to use limited retention techniques. In our approach, we present applications a degraded set of data with a retention delay attached to it which matches both application requirements and users privacy wishes. Data degradation can be twofold: the accuracy of context data can be lowered such that the less privacy sensitive parts are retained, and context data can be transformed such that only particular abilities for application remain available. Retention periods can be specified to trigger irreversible removal of the context data from the system
Publishing Microdata with a Robust Privacy Guarantee
Today, the publication of microdata poses a privacy threat. Vast research has
striven to define the privacy condition that microdata should satisfy before it
is released, and devise algorithms to anonymize the data so as to achieve this
condition. Yet, no method proposed to date explicitly bounds the percentage of
information an adversary gains after seeing the published data for each
sensitive value therein. This paper introduces beta-likeness, an appropriately
robust privacy model for microdata anonymization, along with two anonymization
schemes designed therefor, the one based on generalization, and the other based
on perturbation. Our model postulates that an adversary's confidence on the
likelihood of a certain sensitive-attribute (SA) value should not increase, in
relative difference terms, by more than a predefined threshold. Our techniques
aim to satisfy a given beta threshold with little information loss. We
experimentally demonstrate that (i) our model provides an effective privacy
guarantee in a way that predecessor models cannot, (ii) our generalization
scheme is more effective and efficient in its task than methods adapting
algorithms for the k-anonymity model, and (iii) our perturbation method
outperforms a baseline approach. Moreover, we discuss in detail the resistance
of our model and methods to attacks proposed in previous research.Comment: VLDB201
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