4 papers
Refined Differentially Private Linear Regression via Extension of a Free Lunch Result
Sasmita Harini S, Anshoo Tandon
As data-privacy regulations tighten and statistical models are increasingly deployed on sensitive human-sourced data, privacy-preserving linear regression has become a critical nec…
On the Optimal Number of Grids for Differentially Private Non-Interactive -Means Clustering
Gokularam Muthukrishnan, Anshoo Tandon
Differentially private -means clustering enables releasing cluster centers derived from a dataset while protecting the privacy of the individuals. Non-interactive clustering tec…
SKALD: Scalable K-Anonymisation for Large Datasets
Kailash Reddy, Novoneel Chakraborty, Amogh Dharmavaram +1
Data privacy and anonymisation are critical concerns in today's data-driven society, particularly when handling personal and sensitive user data. Regulatory frameworks worldwide re…
Building a Privacy Web with SPIDEr -- Secure Pipeline for Information De-Identification with End-to-End Encryption
Novoneel Chakraborty, Anshoo Tandon, Kailash Reddy +5
Data de-identification makes it possible to glean insights from data while preserving user privacy. The use of Trusted Execution Environments (TEEs) allow for the execution of de-i…