1 citations · 1 across the 8 of their papers we have counts for
9 papers
The Right to Be Forgotten under Correlation: Privacy-Utility Tradeoffs
Luis Massny, Rawad Bitar, Fangwei Ye +1
We study privacy-preserving algorithms that redact information from a correlated dataset. The goal is to reduce the information leakage about sensitive parts. We analytically chara…
Secure Over-the-Air Computation Against Multiple Eavesdroppers using Correlated Artificial Noise
David Nordlund, Luis Maßny, Antonia Wachter-Zeh +2
Over-the-air (OtA) computation enables scalable analog aggregation by exploiting the superposition property of wireless channels, making it an attractive joint communication and co…
ProDiGy: Proximity- and Dissimilarity-Based Byzantine-Robust Federated Learning
Sena Ergisi, Luis Maßny, Rawad Bitar
Federated Learning (FL) emerged as a widely studied paradigm for distributed learning. Despite its many advantages, FL remains vulnerable to adversarial attacks, especially under d…
Between Close Enough to Reveal and Far Enough to Protect: a New Privacy Region for Correlated Data
Luis Maßny, Rawad Bitar, Fangwei Ye +1
When users make personal privacy choices, correlation between their data can cause inadvertent leakage about users who do not want to share their data by other users sharing their…
Interactive Byzantine-Resilient Gradient Coding for General Data Assignments
Shreyas Jain, Luis Maßny, Christoph Hofmeister +2
We tackle the problem of Byzantine errors in distributed gradient descent within the Byzantine-resilient gradient coding framework. Our proposed solution can recover the exact full…
Byzantine-Resilient Gradient Coding through Local Gradient Computations
Christoph Hofmeister, Luis Maßny, Eitan Yaakobi +1
We consider gradient coding in the presence of an adversary controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes…
Trading Communication for Computation in Byzantine-Resilient Gradient Coding
Christoph Hofmeister, Luis Maßny, Eitan Yaakobi +1
We consider gradient coding in the presence of an adversary, controlling so-called malicious workers trying to corrupt the computations. Previous works propose the use of MDS codes…
Secure Over-the-Air Computation using Zero-Forced Artificial Noise
Luis Maßny, Antonia Wachter-Zeh
Over-the-air computation has the potential to increase the communication-efficiency of data-dependent distributed wireless systems, but is vulnerable to eavesdropping. We consider…
Nested Gradient Codes for Straggler Mitigation in Distributed Machine Learning
Luis Maßny, Christoph Hofmeister, Maximilian Egger +2
We consider distributed learning in the presence of slow and unresponsive worker nodes, referred to as stragglers. In order to mitigate the effect of stragglers, gradient coding re…