1 citations · 1 across the 6 of their papers we have counts for
6 papers
Differentially Private and Adversarially Robust Machine Learning: An Empirical Evaluation
Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
Malicious adversaries can attack machine learning models to infer sensitive information or damage the system by launching a series of evasion attacks. Although various work address…
Elevating Defenses: Bridging Adversarial Training and Watermarking for Model Resilience
Janvi Thakkar, Giulio Zizzo, Sergio Maffeis
Machine learning models are being used in an increasing number of critical applications; thus, securing their integrity and ownership is critical. Recent studies observed that adve…
FedSpectral+: Spectral Clustering using Federated Learning
Janvi Thakkar, Devvrat Joshi
Clustering in graphs has been a well-known research problem, particularly because most Internet and social network data is in the form of graphs. Organizations widely use spectral…
k-Means SubClustering: A Differentially Private Algorithm with Improved Clustering Quality
Devvrat Joshi, Janvi Thakkar
In today's data-driven world, the sensitivity of information has been a significant concern. With this data and additional information on the person's background, one can easily in…
Merged-GHCIDR: Geometrical Approach to Reduce Image Data
Devvrat Joshi, Janvi Thakkar, Siddharth Soni +3
The computational resources required to train a model have been increasing since the inception of deep networks. Training neural networks on massive datasets have become a challeng…
Geometrical Homogeneous Clustering for Image Data Reduction
Shril Mody, Janvi Thakkar, Devvrat Joshi +3
In this paper, we present novel variations of an earlier approach called homogeneous clustering algorithm for reducing dataset size. The intuition behind the approaches proposed in…