16,552 research outputs found
Privacy-preserving Publication of Mobility Data with High Utility
An increasing amount of mobility data is being collected every day by
different means, e.g., by mobile phone operators. This data is sometimes
published after the application of simple anonymization techniques, which might
lead to severe privacy threats. We propose in this paper a new solution whose
novelty is twofold. Firstly, we introduce an algorithm designed to hide places
where a user stops during her journey (namely points of interest), by enforcing
a constant speed along her trajectory. Secondly, we leverage places where users
meet to take a chance to swap their trajectories and therefore confuse an
attacker.Comment: 2015 35th IEEE International Conference on Distributed Computed
System
Time Distortion Anonymization for the Publication of Mobility Data with High Utility
An increasing amount of mobility data is being collected every day by
different means, such as mobile applications or crowd-sensing campaigns. This
data is sometimes published after the application of simple anonymization
techniques (e.g., putting an identifier instead of the users' names), which
might lead to severe threats to the privacy of the participating users.
Literature contains more sophisticated anonymization techniques, often based on
adding noise to the spatial data. However, these techniques either compromise
the privacy if the added noise is too little or the utility of the data if the
added noise is too strong. We investigate in this paper an alternative
solution, which builds on time distortion instead of spatial distortion.
Specifically, our contribution lies in (1) the introduction of the concept of
time distortion to anonymize mobility datasets (2) Promesse, a protection
mechanism implementing this concept (3) a practical study of Promesse compared
to two representative spatial distortion mechanisms, namely Wait For Me, which
enforces k-anonymity, and Geo-Indistinguishability, which enforces differential
privacy. We evaluate our mechanism practically using three real-life datasets.
Our results show that time distortion reduces the number of points of interest
that can be retrieved by an adversary to under 3 %, while the introduced
spatial error is almost null and the distortion introduced on the results of
range queries is kept under 13 % on average.Comment: in 14th IEEE International Conference on Trust, Security and Privacy
in Computing and Communications, Aug 2015, Helsinki, Finlan
Measuring Membership Privacy on Aggregate Location Time-Series
While location data is extremely valuable for various applications,
disclosing it prompts serious threats to individuals' privacy. To limit such
concerns, organizations often provide analysts with aggregate time-series that
indicate, e.g., how many people are in a location at a time interval, rather
than raw individual traces. In this paper, we perform a measurement study to
understand Membership Inference Attacks (MIAs) on aggregate location
time-series, where an adversary tries to infer whether a specific user
contributed to the aggregates.
We find that the volume of contributed data, as well as the regularity and
particularity of users' mobility patterns, play a crucial role in the attack's
success. We experiment with a wide range of defenses based on generalization,
hiding, and perturbation, and evaluate their ability to thwart the attack
vis-a-vis the utility loss they introduce for various mobility analytics tasks.
Our results show that some defenses fail across the board, while others work
for specific tasks on aggregate location time-series. For instance, suppressing
small counts can be used for ranking hotspots, data generalization for
forecasting traffic, hotspot discovery, and map inference, while sampling is
effective for location labeling and anomaly detection when the dataset is
sparse. Differentially private techniques provide reasonable accuracy only in
very specific settings, e.g., discovering hotspots and forecasting their
traffic, and more so when using weaker privacy notions like crowd-blending
privacy. Overall, our measurements show that there does not exist a unique
generic defense that can preserve the utility of the analytics for arbitrary
applications, and provide useful insights regarding the disclosure of sanitized
aggregate location time-series
Quantification of De-anonymization Risks in Social Networks
The risks of publishing privacy-sensitive data have received considerable
attention recently. Several de-anonymization attacks have been proposed to
re-identify individuals even if data anonymization techniques were applied.
However, there is no theoretical quantification for relating the data utility
that is preserved by the anonymization techniques and the data vulnerability
against de-anonymization attacks.
In this paper, we theoretically analyze the de-anonymization attacks and
provide conditions on the utility of the anonymized data (denoted by anonymized
utility) to achieve successful de-anonymization. To the best of our knowledge,
this is the first work on quantifying the relationships between anonymized
utility and de-anonymization capability. Unlike previous work, our
quantification analysis requires no assumptions about the graph model, thus
providing a general theoretical guide for developing practical
de-anonymization/anonymization techniques.
Furthermore, we evaluate state-of-the-art de-anonymization attacks on a
real-world Facebook dataset to show the limitations of previous work. By
comparing these experimental results and the theoretically achievable
de-anonymization capability derived in our analysis, we further demonstrate the
ineffectiveness of previous de-anonymization attacks and the potential of more
powerful de-anonymization attacks in the future.Comment: Published in International Conference on Information Systems Security
and Privacy, 201
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