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A Neural Approach to Spatio-Temporal Data Release with User-Level Differential Privacy. (arXiv:2208.09744v1 [cs.DB])
Aug. 23, 2022, 1:20 a.m. | Ritesh Ahuja, Sepanta Zeighami, Gabriel Ghinita, Cyrus Shahabi
cs.CR updates on arXiv.org arxiv.org
Several companies (e.g., Meta, Google) have initiated "data-for-good"
projects where aggregate location data are first sanitized and released
publicly, which is useful to many applications in transportation, public health
(e.g., COVID-19 spread) and urban planning. Differential privacy (DP) is the
protection model of choice to ensure the privacy of the individuals who
generated the raw location data. However, current solutions fail to preserve
data utility when each individual contributes multiple location reports (i.e.,
under user-level privacy). To offset this limitation, …
More from arxiv.org / cs.CR updates on arXiv.org
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