CRJul 16, 2019

Location Privacy in Conservation

arXiv:1907.07054v1
Originality Synthesis-oriented
AI Analysis

This work tackles privacy risks for endangered species in conservation data sharing, offering a formal method to balance data utility with protection against abuse.

The paper addresses the conflict between sharing spatial data for conservation research and protecting endangered species from wildlife criminals by applying geo-indistinguishability to add noise to published data, enabling quantification of the privacy-utility tradeoff.

The growing public nature of academic journals along with current best practices of sharing primary data for scientific research are profoundly valuable for the understanding of a species and their conservation efforts. On the other hand, public spatial data on endangered species may be easily abused by wildlife criminals. In this paper, we discuss how geo-indistinguishability, a formal notion of privacy for location-based systems, can be used to add noise to published spatial data whilst allowing quantification of such tradeoff.

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