42,023 research outputs found
InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
Personal photos of individuals when shared online, apart from exhibiting a
myriad of memorable details, also reveals a wide range of private information
and potentially entails privacy risks (e.g., online harassment, tracking). To
mitigate such risks, it is crucial to study techniques that allow individuals
to limit the private information leaked in visual data. We tackle this problem
in a novel image obfuscation framework: to maximize entropy on inferences over
targeted privacy attributes, while retaining image fidelity. We approach the
problem based on an encoder-decoder style architecture, with two key novelties:
(a) introducing a discriminator to perform bi-directional translation
simultaneously from multiple unpaired domains; (b) predicting an image
interpolation which maximizes uncertainty over a target set of attributes. We
find our approach generates obfuscated images faithful to the original input
images, and additionally increase uncertainty by 6.2 (or up to 0.85
bits) over the non-obfuscated counterparts.Comment: 20 pages, 7 figure
Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
As the modern world becomes increasingly digitized and interconnected,
distributed signal processing has proven to be effective in processing its
large volume of data. However, a main challenge limiting the broad use of
distributed signal processing techniques is the issue of privacy in handling
sensitive data. To address this privacy issue, we propose a novel yet general
subspace perturbation method for privacy-preserving distributed optimization,
which allows each node to obtain the desired solution while protecting its
private data. In particular, we show that the dual variables introduced in each
distributed optimizer will not converge in a certain subspace determined by the
graph topology. Additionally, the optimization variable is ensured to converge
to the desired solution, because it is orthogonal to this non-convergent
subspace. We therefore propose to insert noise in the non-convergent subspace
through the dual variable such that the private data are protected, and the
accuracy of the desired solution is completely unaffected. Moreover, the
proposed method is shown to be secure under two widely-used adversary models:
passive and eavesdropping. Furthermore, we consider several distributed
optimizers such as ADMM and PDMM to demonstrate the general applicability of
the proposed method. Finally, we test the performance through a set of
applications. Numerical tests indicate that the proposed method is superior to
existing methods in terms of several parameters like estimated accuracy,
privacy level, communication cost and convergence rate
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