{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T02:28:59Z","timestamp":1768271339101,"version":"3.49.0"},"reference-count":0,"publisher":"Privacy Enhancing Technologies Symposium Advisory Board","issue":"1","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["PoPETs"],"abstract":"<jats:p>We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a server, thus raising privacy concerns. Here, we propose a Federated KDE framework for estimating the user population density, which not only keeps location data on the devices but also provides probabilistic privacy guarantees against a malicious server that tries to infer users' location. Our approach Federated random Fourier feature (RFF) KDE leverages a random feature representation of the KDE solution, in which each user's information is irreversibly projected onto a small number of spatially delocalized basis functions, making precise localization impossible while still allowing population density estimation. We evaluate our method on both synthetic and real-world datasets, and we show that it achieves a better utility (estimation performance)-vs-privacy (distance between inferred and true locations) tradeoff, compared to state-of-the-art baselines (e.g., GeoInd). We also vary the number of basis functions per user, to further improve the privacy-utility trade-off, and we provide analytical bounds on localization as a function of areal unit size and kernel bandwidth.<\/jats:p>","DOI":"10.56553\/popets-2023-0019","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T16:43:52Z","timestamp":1673541832000},"page":"309-324","source":"Crossref","is-referenced-by-count":5,"title":["Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features"],"prefix":"10.56553","volume":"2023","author":[{"given":"Zixiao","family":"Zong","sequence":"first","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengwei","family":"Yang","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Justin","family":"Ley","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Athina","family":"Markopoulou","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carter","family":"Butts","sequence":"additional","affiliation":[{"name":"University of California, Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"35752","published-online":{"date-parts":[[2023,1]]},"container-title":["Proceedings on Privacy Enhancing Technologies"],"original-title":[],"deposited":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T16:44:11Z","timestamp":1673541851000},"score":1,"resource":{"primary":{"URL":"https:\/\/petsymposium.org\/popets\/2023\/popets-2023-0019.php"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1]]},"references-count":0,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["10.56553\/popets-2023-0019"],"URL":"https:\/\/doi.org\/10.56553\/popets-2023-0019","relation":{},"ISSN":["2299-0984"],"issn-type":[{"value":"2299-0984","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1]]}}}