20 citations · 22 across the 10 of their papers we have counts for
10 papers
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…
Integrating OTFS in Airplane-Aided Next-Generation Networking
Ashok S Kumar, Shashank Shekhar, Gokularam Muthukrishnan +2
Next-generation networks explore the opportunistic assistance of airliner/high-altitude platforms (HAPs) in delivering high data rates for terrestrial networks to ensure consistent…
Tuning-Free Online Robust Principal Component Analysis through Implicit Regularization
Lakshmi Jayalal, Gokularam Muthukrishnan, Sheetal Kalyani
The performance of the standard Online Robust Principal Component Analysis (OR-PCA) technique depends on the optimum tuning of the explicit regularizers and this tuning is dataset…
Source Enumeration using the Distribution of Angles: A Robust and Parameter-Free Approach
Gokularam Muthukrishnan, Siva Shanmugam, Sheetal Kalyani
Source enumeration, the task of estimating the number of sources from the signal received by the array of antennas, is a critical problem in array signal processing. Numerous metho…
Low-Complexity Linear Decoupling of Users for Uplink Massive MU-MIMO Detection
S. Sowmya, Gokularam Muthukrishnan, K. Giridhar
Massive MIMO (mMIMO) enables users with different requirements to get connected to the same base station (BS) on the same set of resources. In the uplink of Multiuser massive MIMO…
Using DCFT for Multi-Target Detection in Distributed Radar Systems with Several Transmitters
Gokularam Muthukrishnan, S. Sruti, K. Giridhar
In distributed radar systems, when several transmitters radiate simultaneously, the reflected signals need to be distinguished at the receivers to detect various targets. If the tr…
Differential Privacy with Higher Utility by Exploiting Coordinate-wise Disparity: Laplace Mechanism Can Beat Gaussian in High Dimensions
Gokularam Muthukrishnan, Sheetal Kalyani
Conventionally, in a differentially private additive noise mechanism, independent and identically distributed (i.i.d.) noise samples are added to each coordinate of the response. I…
Grafting Laplace and Gaussian distributions: A new noise mechanism for differential privacy
Gokularam Muthukrishnan, Sheetal Kalyani
The framework of differential privacy protects an individual's privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differ…
Introducing the Huber mechanism for differentially private low-rank matrix completion
R Adithya Gowtham, Gokularam M, Thulasi Tholeti +1
Performing low-rank matrix completion with sensitive user data calls for privacy-preserving approaches. In this work, we propose a novel noise addition mechanism for preserving dif…
Subspace clustering without knowing the number of clusters: A parameter free approach
Vishnu Menon, Gokularam M, Sheetal Kalyani
Subspace clustering, the task of clustering high dimensional data when the data points come from a union of subspaces is one of the fundamental tasks in unsupervised machine learni…