{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T11:00:58Z","timestamp":1770289258821,"version":"3.49.0"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T00:00:00Z","timestamp":1704758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["K\u00fcnstl Intell"],"published-print":{"date-parts":[[2024,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Valuable insights, such as frequently visited environments in the wake of the COVID-19 pandemic, can oftentimes only be gained by analyzing sensitive data spread across edge-devices like smartphones. To facilitate such an analysis, we present a toolchain called\u00a0PrivAgE\u00a0for a distributed, privacy-preserving aggregation of local data by taking the limited resources of edge-devices into account. The distributed aggregation is based on secure summation and simultaneously satisfies the notion of differential privacy. In this way, other parties can neither learn the sensitive data of single clients nor a single client\u2019s influence on the final result. We perform an evaluation of the power consumption, the running time and the bandwidth overhead on real as well as simulated devices and demonstrate the flexibility of our toolchain by presenting an extension of the summation of histograms to distributed clustering.<\/jats:p>","DOI":"10.1007\/s13218-023-00823-8","type":"journal-article","created":{"date-parts":[[2024,1,9]],"date-time":"2024-01-09T14:02:10Z","timestamp":1704808930000},"page":"183-188","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["PrivAgE: A Toolchain for Privacy-Preserving Distributed Aggregation on Edge-Devices"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-0911-2582","authenticated-orcid":false,"given":"Johannes","family":"Liebenow","sequence":"first","affiliation":[]},{"given":"Timothy","family":"Imort","sequence":"additional","affiliation":[]},{"given":"Yannick","family":"Fuchs","sequence":"additional","affiliation":[]},{"given":"Marcel","family":"Heisel","sequence":"additional","affiliation":[]},{"given":"Nadja","family":"K\u00e4ding","sequence":"additional","affiliation":[]},{"given":"Jan","family":"Rupp","sequence":"additional","affiliation":[]},{"given":"Esfandiar","family":"Mohammadi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,1,9]]},"reference":[{"key":"823_CR1","doi-asserted-by":"crossref","unstructured":"Alo UR, Nkwo FO, Nweke HF, Achi II, Okemiri HA (2021) Non-pharmaceutical interventions against covid-19 pandemic: review of contact tracing and social distancing technologies, protocols, apps, security and open research directions. Sensors 22","DOI":"10.3390\/s22010280"},{"key":"823_CR2","doi-asserted-by":"crossref","unstructured":"Bell JH, Bonawitz KA, Gasc\u00f3n A, Lepoint T, Raykova M (2020) Secure single-server aggregation with (poly) logarithmic overhead. In: ACM SIGSAC Conference on Computer and Communications Security","DOI":"10.1145\/3372297.3417885"},{"key":"823_CR3","unstructured":"Beutel DJ, Topal T, Mathur A, Qiu X, Fernandez-Marques J, Gao Y, Sani L, Li KH, Parcollet T, de\u00a0Gusm\u00e3o, P.P.B., et\u00a0al.: Flower: A friendly federated learning research framework. arXiv preprint arXiv:2007.14390 (2020)"},{"key":"823_CR4","unstructured":"Chang A, Ghazi B, Kumar R, Manurangsi P (2021) Locally private k-means in one round. In: International Conference on Machine Learning, PMLR"},{"key":"823_CR5","doi-asserted-by":"crossref","unstructured":"Dwork C (2006) Differential privacy. In: Automata, Languages and Programming: 33rd International Colloquium, ICALP, Proceedings, Springer","DOI":"10.1007\/11787006_1"},{"key":"823_CR6","unstructured":"EU: General Data Protection Regulation. [Online], Available: https:\/\/gdpr-info.eu\/"},{"key":"823_CR7","doi-asserted-by":"crossref","unstructured":"Fereidooni H, Marchal S, Miettinen M, Mirhoseini A, M\u00f6llering H, Nguyen TD, Rieger P, Sadeghi AR, Schneider T, Yalame H, et al (2021) Safelearn: Secure aggregation for private federated learning. In: IEEE Security and Privacy Workshops (SPW)","DOI":"10.1109\/SPW53761.2021.00017"},{"key":"823_CR8","doi-asserted-by":"publisher","unstructured":"Geng Q, Viswanath P (2016) The optimal noise-adding mechanism in differential privacy. IEEE Trans Inform Theory 62. https:\/\/doi.org\/10.1109\/TIT.2015.2504967","DOI":"10.1109\/TIT.2015.2504967"},{"key":"823_CR9","unstructured":"Holohan N, Braghin S, Mac Aonghusa P, Levacher K (2019) Diffprivlib: the IBM differential privacy library. ArXiv e-prints 1907.02444 [cs.CR]"},{"key":"823_CR10","unstructured":"Huba D, Nguyen J, Malik K, Zhu R, Rabbat M, Yousefpour A, Wu CJ, Zhan H, Ustinov P, Srinivas H et al (2022) Papaya: practical, private, and scalable federated learning. Proceed Machine Learn Syst"},{"key":"823_CR11","doi-asserted-by":"crossref","unstructured":"Truex S, Baracaldo N, Anwar A, Steinke T, Ludwig H, Zhang R, Zhou Y (2019) A hybrid approach to privacy-preserving federated learning. In: Proceedings of the 12th ACM workshop on artificial intelligence and security","DOI":"10.1145\/3338501.3357370"},{"key":"823_CR12","unstructured":"Zhu W, Kairouz P, McMahan B, Sun H, Li W (2020) Federated heavy hitters discovery with differential privacy. In: International Conference on Artificial Intelligence and Statistics, PMLR"}],"container-title":["KI - K\u00fcnstliche Intelligenz"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-023-00823-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13218-023-00823-8\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13218-023-00823-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,5]],"date-time":"2024-12-05T22:19:51Z","timestamp":1733437191000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13218-023-00823-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,9]]},"references-count":12,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2024,11]]}},"alternative-id":["823"],"URL":"https:\/\/doi.org\/10.1007\/s13218-023-00823-8","relation":{},"ISSN":["0933-1875","1610-1987"],"issn-type":[{"value":"0933-1875","type":"print"},{"value":"1610-1987","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,9]]},"assertion":[{"value":"19 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 November 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 January 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}