Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
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 […]
What makes consent meaningful?
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 […]
Vogue: Faster computation of private heavy hitters
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 […]
Shield: Secure Allegation Escrow System with Stronger Guarantees
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 […]
Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
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 […]
Winter Conference on Applications of Computer Vision (WACV), 2024
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 […]
Empowering SMPC: Bridging the Gap Between Scalability, Memory Efficiency and Privacy in Neural Network Inference
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
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 […]
SKALD: Scalable K-Anonymisation for Large Datasets
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 […]
Improving the Privacy Loss Under User-Level DP Composition for Fixed Estimation Error
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 […]