Authors: Sasmita Harini S, Anshoo Tandon On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other […]

Authors: Asilata Karandikar On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream tasks without […]

Authors: Pranav Jangir, Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal, Somya Sangal On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because […]

On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Authors: Nishat Koti, Varsha Bhat Kukkala, Arpita Patra, Bhavish Raj Gopal Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis […]

On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Authors: Pranjal Naman, Yogesh Simmhan Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream […]

On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Authors: M. Yashwanth, Gaurav Kumar Nayak, Harsh Rangwani, Arya Singh, R. Venkatesh Babu, Anirban Chakraborty Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because […]

On the Optimal Number of Grids for Differentially Private Non-Interactive K-Means Clustering Authors: Ramya Burra, Anshoo Tandon, Srishti Mittal Differentially private K-means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for […]

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 protecting the privacy of the individuals. Non-interactive clustering techniques based on privatized histograms are attractive because the released data synopsis can be reused for other downstream […]

Authors:K. Reddy, N. Chakraborty, A. Dharmavaram, A. Tandon Know more Data privacy and anonymisation are critical concerns in today’s data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide recommend privacy-preserving protocols such as k-anonymisation to de-identify releases of tabular data. Available hardware resources provide an upper bound on the maximum size […]

Authors:V. A. Rameshwar and A. Tandon Know more This paper considers the private release of statistics of several disjoint subsets of a datasets. In particular, we consider the epsilon-user-level differentially private release of sample means and variances of sample values in disjoint subsets of a dataset, in a potentially sequential manner. Traditional analysis of the privacy […]

Authors:V. A. Rameshwar and A. Tandon Know more In this paper, we consider the problem of degradation of anonymity upon linkages of anonymized datasets. We work in the setting where an adversary links together tgeq 2 anonymized datasets in which a user of interest participates, based on the user’s known quasi-identifiers, which motivates the use of ell-diversity as […]

Authors: V. Arvind Rameshwar (IIT Madras) and Anshoo Tandon Know more We revisit the problem of releasing the sample mean of bounded samples in a dataset, privately, under user-level ε-differential privacy (DP). We aim to derive the optimal method of preprocessing data samples, within a canonical class of processing strategies, in terms of the estimation error. […]