24,142 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
Semi-Adversarial Networks: Convolutional Autoencoders for Imparting Privacy to Face Images
In this paper, we design and evaluate a convolutional autoencoder that
perturbs an input face image to impart privacy to a subject. Specifically, the
proposed autoencoder transforms an input face image such that the transformed
image can be successfully used for face recognition but not for gender
classification. In order to train this autoencoder, we propose a novel training
scheme, referred to as semi-adversarial training in this work. The training is
facilitated by attaching a semi-adversarial module consisting of a pseudo
gender classifier and a pseudo face matcher to the autoencoder. The objective
function utilized for training this network has three terms: one to ensure that
the perturbed image is a realistic face image; another to ensure that the
gender attributes of the face are confounded; and a third to ensure that
biometric recognition performance due to the perturbed image is not impacted.
Extensive experiments confirm the efficacy of the proposed architecture in
extending gender privacy to face images
Privacy-Preserving Adversarial Networks
We propose a data-driven framework for optimizing privacy-preserving data
release mechanisms to attain the information-theoretically optimal tradeoff
between minimizing distortion of useful data and concealing specific sensitive
information. Our approach employs adversarially-trained neural networks to
implement randomized mechanisms and to perform a variational approximation of
mutual information privacy. We validate our Privacy-Preserving Adversarial
Networks (PPAN) framework via proof-of-concept experiments on discrete and
continuous synthetic data, as well as the MNIST handwritten digits dataset. For
synthetic data, our model-agnostic PPAN approach achieves tradeoff points very
close to the optimal tradeoffs that are analytically-derived from model
knowledge. In experiments with the MNIST data, we visually demonstrate a
learned tradeoff between minimizing the pixel-level distortion versus
concealing the written digit.Comment: 16 page
Security Evaluation of Support Vector Machines in Adversarial Environments
Support Vector Machines (SVMs) are among the most popular classification
techniques adopted in security applications like malware detection, intrusion
detection, and spam filtering. However, if SVMs are to be incorporated in
real-world security systems, they must be able to cope with attack patterns
that can either mislead the learning algorithm (poisoning), evade detection
(evasion), or gain information about their internal parameters (privacy
breaches). The main contributions of this chapter are twofold. First, we
introduce a formal general framework for the empirical evaluation of the
security of machine-learning systems. Second, according to our framework, we
demonstrate the feasibility of evasion, poisoning and privacy attacks against
SVMs in real-world security problems. For each attack technique, we evaluate
its impact and discuss whether (and how) it can be countered through an
adversary-aware design of SVMs. Our experiments are easily reproducible thanks
to open-source code that we have made available, together with all the employed
datasets, on a public repository.Comment: 47 pages, 9 figures; chapter accepted into book 'Support Vector
Machine Applications
An Economic Analysis of Privacy Protection and Statistical Accuracy as Social Choices
Statistical agencies face a dual mandate to publish accurate statistics while protecting respondent privacy. Increasing privacy protection requires decreased accuracy. Recognizing this as a resource allocation problem, we propose an economic solution: operate where the marginal cost of increasing privacy equals the marginal benefit. Our model of production, from computer science, assumes data are published using an efficient differentially private algorithm. Optimal choice weighs the demand for accurate statistics against the demand for privacy. Examples from U.S. statistical programs show how our framework can guide decision-making. Further progress requires a better understanding of willingness-to-pay for privacy and statistical accuracy
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