2,846 research outputs found
Towards trajectory anonymization: a generalization-based approach
Trajectory datasets are becoming popular due to the massive usage of GPS and locationbased services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity to trajectories and propose a novel generalization-based approach for anonymization of trajectories. We further show that releasing
anonymized trajectories may still have some privacy leaks. Therefore we propose a randomization based reconstruction algorithm for releasing anonymized trajectory data and also present how the underlying techniques can be adapted to other anonymity standards. The experimental results on real and synthetic trajectory datasets show the effectiveness of the proposed techniques
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
Asymptotic Loss in Privacy due to Dependency in Gaussian Traces
The rapid growth of the Internet of Things (IoT) necessitates employing
privacy-preserving techniques to protect users' sensitive information. Even
when user traces are anonymized, statistical matching can be employed to infer
sensitive information. In our previous work, we have established the privacy
requirements for the case that the user traces are instantiations of discrete
random variables and the adversary knows only the structure of the dependency
graph, i.e., whether each pair of users is connected. In this paper, we
consider the case where data traces are instantiations of Gaussian random
variables and the adversary knows not only the structure of the graph but also
the pairwise correlation coefficients. We establish the requirements on
anonymization to thwart such statistical matching, which demonstrate the
significant degree to which knowledge of the pairwise correlation coefficients
further significantly aids the adversary in breaking user anonymity.Comment: IEEE Wireless Communications and Networking Conferenc
Location Anonymization With Considering Errors and Existence Probability
Mobile devices that can sense their location using GPS or Wi-Fi have become extremely popular. However, many users hesitate to provide their accurate location information to unreliable third parties if it means that their identities or sensitive attribute values will be disclosed by doing so. Many approaches for anonymization, such as k-anonymity, have been proposed to tackle this issue. Existing studies for k-anonymity usually anonymize each user\u27s location so that the anonymized area contains k or more users. Existing studies, however, do not consider location errors and the probability that each user actually exists at the anonymized area. As a result, a specific user might be identified by untrusted third parties. We propose novel privacy and utility metrics that can treat the location and an efficient algorithm to anonymize the information associated with users\u27 locations. This is the first work that anonymizes location while considering location errors and the probability that each user is actually present at the anonymized area. By means of simulations, we have proven that our proposed method can reduce the risk of the user\u27s attributes being identified while maintaining the utility of the anonymized data
Individual Privacy vs Population Privacy: Learning to Attack Anonymization
Over the last decade there have been great strides made in developing
techniques to compute functions privately. In particular, Differential Privacy
gives strong promises about conclusions that can be drawn about an individual.
In contrast, various syntactic methods for providing privacy (criteria such as
kanonymity and l-diversity) have been criticized for still allowing private
information of an individual to be inferred. In this report, we consider the
ability of an attacker to use data meeting privacy definitions to build an
accurate classifier. We demonstrate that even under Differential Privacy, such
classifiers can be used to accurately infer "private" attributes in realistic
data. We compare this to similar approaches for inferencebased attacks on other
forms of anonymized data. We place these attacks on the same scale, and observe
that the accuracy of inference of private attributes for Differentially Private
data and l-diverse data can be quite similar
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