Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data

Abstract : Individuals share increasing amounts of personal data online. This data often involves–or at least has privacy implications for–data subjects other than the individuals who shares it (e.g., photos, genomic data) and the data is shared without their consent. A sadly popular example, with dramatic consequences, is revenge pornography. In this paper, we propose ConsenShare, a system for sharing, in a consensual (wrt the data subjects) and privacy-preserving (wrt both service providers and other individuals) way, data involving subjects other than the uploader. We describe a complete design and implementation of ConsenShare for photos, which relies on image processing and cryptographic techniques, as well as on a two-tier architecture (one entity for detecting the data subjects and contacting them; one entity for hosting the data and for collecting consent). We benchmark the performance (CPU and bandwidth) of ConsenShare by using a dataset of 20k photos from Flickr. We also conduct a survey targeted at Facebook users (N = 321). Our results are quite encouraging: The experimental results demonstrate the feasibility of our approach (i.e., acceptable overheads) and the survey results demonstrate a potential interest from the users.
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Alexandra-Mihaela Olteanu, Kévin Huguenin, Italo Dacosta, Jean-Pierre Hubaux. Consensual and Privacy-Preserving Sharing of Multi-Subject and Interdependent Data. 25th Network and Distributed System Security Symposium (NDSS), Feb 2018, San Diego, CA, United States. ⟨10.14722/ndss.2018.23002⟩. ⟨hal-01644466⟩

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