{"id":20403,"date":"2025-01-23T10:32:06","date_gmt":"2025-01-23T10:32:06","guid":{"rendered":"https:\/\/dataforpublicgood.org.in\/?page_id=20403"},"modified":"2025-01-23T11:56:03","modified_gmt":"2025-01-23T11:56:03","slug":"data-privacy-main-page","status":"publish","type":"page","link":"https:\/\/dataforpublicgood.org.in\/cdpg\/data-privacy-main-page\/","title":{"rendered":"Data Privacy main page"},"content":{"rendered":"\t\t
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\r\n\tOn the Optimal Number of Grids for Differentially Private Non-Interactive...<\/a>\r\n<\/h3>\t\t\t
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On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Authors: Gokularam M, Anshoo Tandon Differentially private K-means clustering enables releasing cluster centers derived from a dataset while...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\tSKALD: Scalable K-Anonymisation for Large Datasets\u00a0<\/a>\r\n<\/h3>\t\t\t
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SKALD: Scalable K-Anonymisation for Large Datasets\u00a0 Authors:K. Reddy, N. Chakraborty, A. Dharmavaram, A. Tandon Data privacy and anonymisation are critical concerns in today’s data-driven society, particularly when handling personal...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\tImproving the Privacy Loss Under User-Level DP Composition for Fixed...<\/a>\r\n<\/h3>\t\t\t
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Improving the Privacy Loss Under User-Level DP Composition for Fixed Estimation Error Authors:V. A. Rameshwar and A. Tandon This paper considers the private release of statistics of several disjoint...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\t\u2113, \ud835\udeff)-Diversity: Linkage-Robustness via a Composition Theorem<\/a>\r\n<\/h3>\t\t\t
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\u2113, \ud835\udeff)-Diversity: Linkage-Robustness via a Composition Theorem Authors:V. A. Rameshwar and A. Tandon In this paper, we consider the problem of degradation of anonymity upon linkages of anonymized datasets....<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\tBounding User Contributions for User-Level Differentially Private Mean Estimation<\/a>\r\n<\/h3>\t\t\t
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Bounding User Contributions for User-Level Differentially Private Mean Estimation Authors: V. Arvind Rameshwar (IIT Madras) and Anshoo Tandon We revisit the problem of releasing the sample mean of bounded...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\tOn the Optimal Number of Grids for Differentially Private Non-Interactive...<\/a>\r\n<\/h3>\t\t\t
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On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering – Data Privacy Authors: Gokularam M, Anshoo Tandon Differentially private K-means clustering enables releasing cluster centers derived...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\tBreaking Data Silos: How GDI is Transforming Access to Geospatial...<\/a>\r\n<\/h3>\t\t\t
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Breaking Data Silos: How GDI is Transforming Access to Geospatial Information in India Authors: Bryan Paul Robert, Mahidhar Chellamani, Jyotirmoy Dutta For years, some of India\u2019s most valuable geospatial...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div>

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\r\n\tAdaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning<\/a>\r\n<\/h3>\t\t\t
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Adaptive Self-Distillation for Minimizing Client Drift in Heterogeneous Federated Learning Authors: M. Yashwanth, G. K. Nayak, A. Singh, Y. Simmhan, A. Chakraborty Federated Learning (FL) is a machine learning...<\/p>\r\n<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\t\t<\/div>\n\t\t\t<\/div>\n<\/article><\/div><\/div>

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