{"id":158550,"date":"2009-11-01T00:00:00","date_gmt":"2009-11-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/differentially-private-aggregation-of-distributed-time-series-with-transformation-and-encryption\/"},"modified":"2018-10-16T19:59:23","modified_gmt":"2018-10-17T02:59:23","slug":"differentially-private-aggregation-of-distributed-time-series-with-transformation-and-encryption","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/differentially-private-aggregation-of-distributed-time-series-with-transformation-and-encryption\/","title":{"rendered":"Differentially Private Aggregation of Distributed Time-Series with Transformation and Encryption"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users wish to publish temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party aggregator wishes to run aggregate queries on the data.<\/p>\n<p>To ensure differential privacy for time-series data despite the presence of temporal correlation, we propose the Fourier Perturbation Algorithm (FPA). Standard differential privacy techniques perform poorly for time-series data. To answer n queries, such techniques can result in a noise of Theta(n) to each query answer, making the answers practically useless if <i>n<\/i> is large. Our FPA algorithm perturbs the Discrete Fourier Transform of the query answers. For answering n queries, FPA improves the expected error from Theta(n) to roughly Theta(k) where k is the number of Fourier coefficients that can (approximately) reconstruct all the n query answers. Our experiments show that k << n for many real-life data-sets resulting in a huge error-improvement for FPA.<\/p>\n<p>To deal with the absence of a trusted central server, we propose the Distributed Laplace Perturbation Algorithm (DLPA) to add noise in a distributed way in order to guarantee differential privacy. To the best of our knowledge, DLPA is the first distributed differentially private algorithm that can scale with a large number of users: DLPA outperforms the only other distributed solution for differential privacy proposed so far, by reducing the computational load per user from O(U) to O(1) where U is the number of users.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose the first differentially private aggregation algorithm for distributed time-series data that offers good practical utility without any trusted server. This addresses two important challenges in participatory data-mining applications where (i) individual users wish to publish temporally correlated time-series data (such as location traces, web history, personal health data), and (ii) an untrusted third-party [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Microsoft 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