1 code implementation • 30 Dec 2023 • Harsh Chaudhari, Anuja Patil, Dhanashree Lavekar, Pranav Khairnar, Raviraj Joshi
This work introduces the L3Cube-MahaSocialNER dataset, the first and largest social media dataset specifically designed for Named Entity Recognition (NER) in the Marathi language.
no code implementations • 3 Dec 2023 • Harsh Chaudhari, Anuja Patil, Dhanashree Lavekar, Pranav Khairnar, Raviraj Joshi, Sachin Pande
In this work, we focus on NER for low-resource language and present our case study in the context of the Indian language Marathi.
no code implementations • 5 Oct 2023 • Harsh Chaudhari, Giorgio Severi, Alina Oprea, Jonathan Ullman
The integration of machine learning (ML) in numerous critical applications introduces a range of privacy concerns for individuals who provide their datasets for model training.
1 code implementation • 25 Aug 2022 • Harsh Chaudhari, John Abascal, Alina Oprea, Matthew Jagielski, Florian Tramèr, Jonathan Ullman
Property inference attacks allow an adversary to extract global properties of the training dataset from a machine learning model.
no code implementations • 20 May 2022 • Harsh Chaudhari, Matthew Jagielski, Alina Oprea
Secure multiparty computation (MPC) has been proposed to allow multiple mutually distrustful data owners to jointly train machine learning (ML) models on their combined data.
no code implementations • 5 Dec 2019 • Harsh Chaudhari, Rahul Rachuri, Ajith Suresh
Machine learning has started to be deployed in fields such as healthcare and finance, which propelled the need for and growth of privacy-preserving machine learning (PPML).
no code implementations • 5 Dec 2019 • Harsh Chaudhari, Ashish Choudhury, Arpita Patra, Ajith Suresh
In this work, we present concretely-efficient protocols for secure $3$-party computation (3PC) over a ring of integers modulo $2^{\ell}$ tolerating one corruption, both with semi-honest and malicious security.