Practical Differential Privacy for Location Data Aggregation using a Hadamard Matrix
暂无分享,去创建一个
[1] Sofya Raskhodnikova,et al. What Can We Learn Privately? , 2008, 2008 49th Annual IEEE Symposium on Foundations of Computer Science.
[2] Pierangela Samarati,et al. Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..
[3] Carmela Troncoso,et al. Unraveling an old cloak: k-anonymity for location privacy , 2010, WPES '10.
[4] Pierangela Samarati,et al. Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .
[5] Raef Bassily,et al. Local, Private, Efficient Protocols for Succinct Histograms , 2015, STOC.
[6] Janardhan Kulkarni,et al. Collecting Telemetry Data Privately , 2017, NIPS.
[7] S L Warner,et al. Randomized response: a survey technique for eliminating evasive answer bias. , 1965, Journal of the American Statistical Association.
[8] Ling Liu,et al. Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms , 2008, IEEE Transactions on Mobile Computing.
[9] Raef Bassily,et al. Practical Locally Private Heavy Hitters , 2017, NIPS.
[10] Hongxia Jin,et al. Private spatial data aggregation in the local setting , 2016, 2016 IEEE 32nd International Conference on Data Engineering (ICDE).
[11] Latanya Sweeney,et al. k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[12] Úlfar Erlingsson,et al. RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response , 2014, CCS.
[13] Huanyu Zhang,et al. Hadamard Response: Estimating Distributions Privately, Efficiently, and with Little Communication , 2018, AISTATS.
[14] Peter Kairouz,et al. Discrete Distribution Estimation under Local Privacy , 2016, ICML.
[15] Chunming Qiao,et al. Mutual Information Optimally Local Private Discrete Distribution Estimation , 2016, ArXiv.
[16] Fan Yang,et al. k-Anonymity Location Privacy Algorithm Based on Clustering , 2018, IEEE Access.
[17] Cynthia Dwork,et al. Calibrating Noise to Sensitivity in Private Data Analysis , 2006, TCC.