57 citations · 122 across the 9 of their papers we have counts for
12 papers
Permissioned LLMs: Enforcing Access Control in Large Language Models
Bargav Jayaraman, Virendra J. Marathe, Hamid Mozaffari +2
In enterprise settings, organizational data is segregated, siloed and carefully protected by elaborate access control frameworks. These access control structures can completely bre…
Semantic Membership Inference Attack against Large Language Models
Hamid Mozaffari, Virendra J. Marathe
Membership Inference Attacks (MIAs) determine whether a specific data point was included in the training set of a target model. In this paper, we introduce the Semantic Membership…
FedPerm: Private and Robust Federated Learning by Parameter Permutation
Hamid Mozaffari, Virendra J. Marathe, Dave Dice
Federated Learning (FL) is a distributed learning paradigm that enables mutually untrusting clients to collaboratively train a common machine learning model. Client data privacy is…
Subject Granular Differential Privacy in Federated Learning
Virendra J. Marathe, Pallika Kanani, Daniel W. Peterson +1
This paper considers subject level privacy in the FL setting, where a subject is an individual whose private information is embodied by several data items either confined within a…
Subject Membership Inference Attacks in Federated Learning
Anshuman Suri, Pallika Kanani, Virendra J. Marathe +1
Privacy attacks on Machine Learning (ML) models often focus on inferring the existence of particular data points in the training data. However, what the adversary really wants to k…
Private Cross-Silo Federated Learning for Extracting Vaccine Adverse Event Mentions
Pallika Kanani, Virendra J. Marathe, Daniel Peterson +2
Federated Learning (FL) is quickly becoming a goto distributed training paradigm for users to jointly train a global model without physically sharing their data. Users can indirect…
Microsecond Consensus for Microsecond Applications
Marcos K. Aguilera, Naama Ben-David, Rachid Guerraoui +3
We consider the problem of making apps fault-tolerant through replication, when apps operate at the microsecond scale, as in finance, embedded computing, and microservices apps. Th…
Efficient Multi-word Compare and Swap
Rachid Guerraoui, Alex Kogan, Virendra J. Marathe +1
Atomic lock-free multi-word compare-and-swap (MCAS) is a powerful tool for designing concurrent algorithms. Yet, its widespread usage has been limited because lock-free implementat…
Private Federated Learning with Domain Adaptation
Daniel Peterson, Pallika Kanani, Virendra J. Marathe
Federated Learning (FL) is a distributed machine learning (ML) paradigm that enables multiple parties to jointly re-train a shared model without sharing their data with any other p…
Correct, Fast Remote Persistence
Sanidhya Kashyap, Dai Qin, Steve Byan +2
Persistence of updates to remote byte-addressable persistent memory (PM), using RDMA operations (RDMA updates), is a poorly understood subject. Visibility of RDMA updates on the re…
The Impact of RDMA on Agreement
Marcos K. Aguilera, Naama Ben-David, Rachid Guerraoui +2
Remote Direct Memory Access (RDMA) is becoming widely available in data centers. This technology allows a process to directly read and write the memory of a remote host, with a mec…
Persistent Memory Transactions
Virendra Marathe, Achin Mishra, Amee Trivedi +8
This paper presents a comprehensive analysis of performance trade offs between implementation choices for transaction runtime systems on persistent memory. We compare three impleme…