{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:12:54Z","timestamp":1760235174104,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T00:00:00Z","timestamp":1627344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>We developed a novel approximate Bayesian computation (ABC) framework, ABCDP, which produces differentially private (DP) and approximate posterior samples. Our framework takes advantage of the sparse vector technique (SVT), widely studied in the differential privacy literature. SVT incurs the privacy cost only when a condition (whether a quantity of interest is above\/below a threshold) is met. If the condition is sparsely met during the repeated queries, SVT can drastically reduce the cumulative privacy loss, unlike the usual case where every query incurs the privacy loss. In ABC, the quantity of interest is the distance between observed and simulated data, and only when the distance is below a threshold can we take the corresponding prior sample as a posterior sample. Hence, applying SVT to ABC is an organic way to transform an ABC algorithm to a privacy-preserving variant with minimal modification, but yields the posterior samples with a high privacy level. We theoretically analyzed the interplay between the noise added for privacy and the accuracy of the posterior samples. We apply ABCDP to several data simulators and show the efficacy of the proposed framework.<\/jats:p>","DOI":"10.3390\/e23080961","type":"journal-article","created":{"date-parts":[[2021,7,27]],"date-time":"2021-07-27T12:18:31Z","timestamp":1627388311000},"page":"961","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["ABCDP: Approximate Bayesian Computation with Differential Privacy"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1771-6104","authenticated-orcid":false,"given":"Mijung","family":"Park","sequence":"first","affiliation":[{"name":"Computer Science, University of British Columbia, Vancouver, BC V6T 1Z4, Canada"}]},{"given":"Margarita","family":"Vinaroz","sequence":"additional","affiliation":[{"name":"Max Planck Institute for Intelligent Systems, 72076 T\u00fcbingen, Germany"},{"name":"Department of Computer Science, University of T\u00fcbingen, 72076 T\u00fcbingen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9400-9262","authenticated-orcid":false,"given":"Wittawat","family":"Jitkrittum","sequence":"additional","affiliation":[{"name":"Google Research, 80636 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1093\/genetics\/145.2.505","article-title":"Inferring coalescence times from DNA sequence data","volume":"145","author":"Balding","year":"1997","journal-title":"Genetics"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Ratmann, O., J\u00f8rgensen, O., Hinkley, T., Stumpf, M., Richardson, S., and Wiuf, C. 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