2,385 research outputs found
Injecting Uncertainty in Graphs for Identity Obfuscation
Data collected nowadays by social-networking applications create fascinating
opportunities for building novel services, as well as expanding our
understanding about social structures and their dynamics. Unfortunately,
publishing social-network graphs is considered an ill-advised practice due to
privacy concerns. To alleviate this problem, several anonymization methods have
been proposed, aiming at reducing the risk of a privacy breach on the published
data, while still allowing to analyze them and draw relevant conclusions. In
this paper we introduce a new anonymization approach that is based on injecting
uncertainty in social graphs and publishing the resulting uncertain graphs.
While existing approaches obfuscate graph data by adding or removing edges
entirely, we propose using a finer-grained perturbation that adds or removes
edges partially: this way we can achieve the same desired level of obfuscation
with smaller changes in the data, thus maintaining higher utility. Our
experiments on real-world networks confirm that at the same level of identity
obfuscation our method provides higher usefulness than existing randomized
methods that publish standard graphs.Comment: VLDB201
Stay Connected, Leave no Trace: Enhancing Security and Privacy in WiFi via Obfuscating Radiometric Fingerprints
The intrinsic hardware imperfection of WiFi chipsets manifests itself in the
transmitted signal, leading to a unique radiometric fingerprint. This
fingerprint can be used as an additional means of authentication to enhance
security. In fact, recent works propose practical fingerprinting solutions that
can be readily implemented in commercial-off-the-shelf devices. In this paper,
we prove analytically and experimentally that these solutions are highly
vulnerable to impersonation attacks. We also demonstrate that such a unique
device-based signature can be abused to violate privacy by tracking the user
device, and, as of today, users do not have any means to prevent such privacy
attacks other than turning off the device.
We propose RF-Veil, a radiometric fingerprinting solution that not only is
robust against impersonation attacks but also protects user privacy by
obfuscating the radiometric fingerprint of the transmitter for non-legitimate
receivers. Specifically, we introduce a randomized pattern of phase errors to
the transmitted signal such that only the intended receiver can extract the
original fingerprint of the transmitter. In a series of experiments and
analyses, we expose the vulnerability of adopting naive randomization to
statistical attacks and introduce countermeasures. Finally, we show the
efficacy of RF-Veil experimentally in protecting user privacy and enhancing
security. More importantly, our proposed solution allows communicating with
other devices, which do not employ RF-Veil.Comment: ACM Sigmetrics 2021 / In Proc. ACM Meas. Anal. Comput. Syst., Vol. 4,
3, Article 44 (December 2020
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