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Protecting sensory data against sensitive inferences
Malekzadeh M., Clegg R., Cavallaro A., Haddadi H.  W-P2DS 2018 (Proceedings of the 1st Workshop on Privacy by Design in Distributed Systems, Porto, Portugal, Apr 23-26, 2018)1-6.2018.Type:Proceedings
Date Reviewed: Feb 20 2020

The paper’s description of a framework for mobile devices to block private information from going to apps includes the problem definition, components, and related experimental results.

Section 1 introduces the guardian-estimator-neutralizer (GEN) framework, a feature learning framework that learns from data to establish tradeoff between user privacy and app utility. The second section gives a formal definition of the problem. Section 3 details the framework’s three components: the neutralizer component is an optimizer with a specific objective function for the tradeoff; the guardian is a feature learning component; and the estimator is for checking accuracy by quantification.

The fourth section presents details of the experiments. Two real-world datasets are used to measure data transformation efficiency and information leakage. The paper ends with related works and a conclusion.

The paper assumes that readers have a background in architectures, machine learning, and optimization algorithms.

Reviewer:  Maulik A. Dave Review #: CR146899 (2006-0138)
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