23 research outputs found
On Collaborative Predictive Blacklisting
Collaborative predictive blacklisting (CPB) allows to forecast future attack
sources based on logs and alerts contributed by multiple organizations.
Unfortunately, however, research on CPB has only focused on increasing the
number of predicted attacks but has not considered the impact on false
positives and false negatives. Moreover, sharing alerts is often hindered by
confidentiality, trust, and liability issues, which motivates the need for
privacy-preserving approaches to the problem. In this paper, we present a
measurement study of state-of-the-art CPB techniques, aiming to shed light on
the actual impact of collaboration. To this end, we reproduce and measure two
systems: a non privacy-friendly one that uses a trusted coordinating party with
access to all alerts (Soldo et al., 2010) and a peer-to-peer one using
privacy-preserving data sharing (Freudiger et al., 2015). We show that, while
collaboration boosts the number of predicted attacks, it also yields high false
positives, ultimately leading to poor accuracy. This motivates us to present a
hybrid approach, using a semi-trusted central entity, aiming to increase
utility from collaboration while, at the same time, limiting information
disclosure and false positives. This leads to a better trade-off of true and
false positive rates, while at the same time addressing privacy concerns.Comment: A preliminary version of this paper appears in ACM SIGCOMM's Computer
Communication Review (Volume 48 Issue 5, October 2018). This is the full
versio
Privacy-Friendly Mobility Analytics using Aggregate Location Data
Location data can be extremely useful to study commuting patterns and
disruptions, as well as to predict real-time traffic volumes. At the same time,
however, the fine-grained collection of user locations raises serious privacy
concerns, as this can reveal sensitive information about the users, such as,
life style, political and religious inclinations, or even identities. In this
paper, we study the feasibility of crowd-sourced mobility analytics over
aggregate location information: users periodically report their location, using
a privacy-preserving aggregation protocol, so that the server can only recover
aggregates -- i.e., how many, but not which, users are in a region at a given
time. We experiment with real-world mobility datasets obtained from the
Transport For London authority and the San Francisco Cabs network, and present
a novel methodology based on time series modeling that is geared to forecast
traffic volumes in regions of interest and to detect mobility anomalies in
them. In the presence of anomalies, we also make enhanced traffic volume
predictions by feeding our model with additional information from correlated
regions. Finally, we present and evaluate a mobile app prototype, called
Mobility Data Donors (MDD), in terms of computation, communication, and energy
overhead, demonstrating the real-world deployability of our techniques.Comment: Published at ACM SIGSPATIAL 201
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
Evaluating Privacy-Friendly Mobility Analytics on Aggregate Location Data
Information about people's movements and the locations they visit enables a wide number of mobility analytics applications, e.g., real-time traffic maps or urban planning, aiming to improve quality of life in modern smart-cities. Alas, the availability of users' fine-grained location data reveals sensitive information about them such as home and work places, lifestyles, political or religious inclinations. In an attempt to mitigate this, aggregation is often employed as a strategy that allows analytics and machine learning tasks while protecting the privacy of individual users' location traces. In this thesis, we perform an end-to-end evaluation of crowdsourced privacy-friendly location aggregation aiming to understand its usefulness for analytics as well as its privacy implications towards users who contribute their data. First, we present a time-series methodology which, along with privacy-friendly crowdsourcing of aggregate locations, supports mobility analytics such as traffic forecasting and mobility anomaly detection. Next, we design quantification frameworks and methodologies that let us reason about the privacy loss stemming from the collection or release of aggregate location information against knowledgeable adversaries that aim to infer users' profiles, locations, or membership. We then utilize these frameworks to evaluate defenses ranging from generalization and hiding, to differential privacy, which can be employed to prevent inferences on aggregate location statistics, in terms of privacy protection as well as utility loss towards analytics tasks. Our results highlight that, while location aggregation is useful for mobility analytics, it is a weak privacy protection mechanism in this setting and that additional defenses can only protect privacy if some statistical utility is sacrificed. Overall, the tools presented in this thesis can be used by providers who desire to assess the quality of privacy protection before data release and its results have several implications about current location data practices and applications
Verifiable Encodings for Secure Homomorphic Analytics
Homomorphic encryption, which enables the execution of arithmetic operations
directly on ciphertexts, is a promising solution for protecting privacy of
cloud-delegated computations on sensitive data. However, the correctness of the
computation result is not ensured. We propose two error detection encodings and
build authenticators that enable practical client-verification of cloud-based
homomorphic computations under different trade-offs and without compromising on
the features of the encryption algorithm. Our authenticators operate on top of
trending ring learning with errors based fully homomorphic encryption schemes
over the integers. We implement our solution in VERITAS, a ready-to-use system
for verification of outsourced computations executed over encrypted data. We
show that contrary to prior work VERITAS supports verification of any
homomorphic operation and we demonstrate its practicality for various
applications, such as ride-hailing, genomic-data analysis, encrypted search,
and machine-learning training and inference.Comment: update authors, typos corrected, scheme update