171 citations · 880 across the 49 of their papers we have counts for
61 papers
Strengthening Recursive Constructions for Zero-Error Shannon Capacity
Ravi Tandon
The exact Shannon capacity is unknown for every odd cycle beyond the five-cycle , making odd cycles a central open problem in zero-error information theory. Improving the know…
Online Context-aware Data Release with Sequence Information Privacy
Bo Jiang, Ming Li, Ravi Tandon
Publishing streaming data in a privacy-preserving manner has been a key research focus for many years. This issue presents considerable challenges, particularly due to the correlat…
Learning Fair Classifiers via Min-Max F-divergence Regularization
Meiyu Zhong, Ravi Tandon
As machine learning (ML) based systems are adopted in domains such as law enforcement, criminal justice, finance, hiring and admissions, ensuring the fairness of ML aided decision-…
Answering Count Queries for Genomic Data with Perfect Privacy
Bo Jiang, Mohamed Seif, Ravi Tandon +1
In this paper, we consider the problem of answering count queries for genomic data subject to perfect privacy constraints. Count queries are often used in applications that collect…
Differentially Private Community Detection for Stochastic Block Models
Mohamed Seif, Dung Nguyen, Anil Vullikanti +1
The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by a…
Unsupervised Change Detection using DRE-CUSUM
Sudarshan Adiga, Ravi Tandon
This paper presents DRE-CUSUM, an unsupervised density-ratio estimation (DRE) based approach to determine statistical changes in time-series data when no knowledge of the pre-and p…
Private Retrieval, Computing and Learning: Recent Progress and Future Challenges
Sennur Ulukus, Salman Avestimehr, Michael Gastpar +3
Most of our lives are conducted in the cyberspace. The human notion of privacy translates into a cyber notion of privacy on many functions that take place in the cyberspace. This a…
FAID Diversity via Neural Networks
Xin Xiao, Nithin Raveendran, Bane Vasic +2
Decoder diversity is a powerful error correction framework in which a collection of decoders collaboratively correct a set of error patterns otherwise uncorrectable by any individu…
Privacy Amplification for Federated Learning via User Sampling and Wireless Aggregation
Mohamed Seif, Wei-Ting Chang, Ravi Tandon
In this paper, we study the problem of federated learning over a wireless channel with user sampling, modeled by a Gaussian multiple access channel, subject to central and local di…
Topological Interference Management with Confidential Messages
Jean de Dieu Mutangana, Ravi Tandon
The topological interference management (TIM) problem refers to the study of the K-user partially connected interference networks with no channel state information at the transmitt…
Adversarial Filters for Secure Modulation Classification
Alex Berian, Kory Staab, Noel Teku +3
Modulation Classification (MC) refers to the problem of classifying the modulation class of a wireless signal. In the wireless communications pipeline, MC is the first operation pe…
Asymmetric Leaky Private Information Retrieval
Islam Samy, Mohamed A. Attia, Ravi Tandon +1
Information-theoretic formulations of the private information retrieval (PIR) problem have been investigated under a variety of scenarios. Symmetric private information retrieval (…
Wireless Federated Learning with Local Differential Privacy
Mohamed Seif, Ravi Tandon, Ming Li
In this paper, we study the problem of federated learning (FL) over a wireless channel, modeled by a Gaussian multiple access channel (MAC), subject to local differential privacy (…
Communication Efficient Federated Learning over Multiple Access Channels
Wei-Ting Chang, Ravi Tandon
In this work, we study the problem of federated learning (FL), where distributed users aim to jointly train a machine learning model with the help of a parameter server (PS). In ea…
Latent-variable Private Information Retrieval
Islam Samy, Mohamed A. Attia, Ravi Tandon +1
In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientatio…
Local Information Privacy and Its Application to Privacy-Preserving Data Aggregation
Bo Jiang, Ming Li, Ravi Tandon
In this paper, we study local information privacy (LIP), and design LIP based mechanisms for statistical aggregation while protecting users' privacy without relying on a trusted th…
Cache-Aided Content Delivery in Fog-RAN Systems with Topological Information and no CSI
Wei-Ting Chang, Ravi Tandon, Osvaldo Simeone
In this work, we consider a Fog Radio Access Network (F-RAN) system with a partially connected wireless topology and no channel state information available at the cloud and Edge No…
On the Upload versus Download Cost for Secure and Private Matrix Multiplication
Wei-Ting Chang, Ravi Tandon
In this paper, we study the problem of secure and private distributed matrix multiplication. Specifically, we focus on a scenario where a user wants to compute the product of a con…
Random Sampling for Distributed Coded Matrix Multiplication
Wei-Ting Chang, Ravi Tandon
Matrix multiplication is a fundamental building block for large scale computations arising in various applications, including machine learning. There has been significant recent in…
On the Capacity of Secure Distributed Matrix Multiplication
Wei-Ting Chang, Ravi Tandon
Matrix multiplication is one of the key operations in various engineering applications. Outsourcing large-scale matrix multiplication tasks to multiple distributed servers or cloud…
The Capacity of Private Information Retrieval from Uncoded Storage Constrained Databases
Mohamed Adel Attia, Deepak Kumar, Ravi Tandon
Private information retrieval (PIR) allows a user to retrieve a desired message from a set of databases without revealing the identity of the desired message. The replicated databa…
Context-aware Data Aggregation with Localized Information Privacy
Bo Jiang, Ming Li, Ravi Tandon
In this paper, localized information privacy (LIP) is proposed, as a new privacy definition, which allows statistical aggregation while protecting users' privacy without relying on…
Near Optimal Coded Data Shuffling for Distributed Learning
Mohamed A. Attia, Ravi Tandon
Data shuffling between distributed cluster of nodes is one of the critical steps in implementing large-scale learning algorithms. Randomly shuffling the data-set among a cluster of…
Secure Retrospective Interference Alignment
Mohamed Seif, Ravi Tandon, Ming Li
In this paper, the -user interference channel with secrecy constraints is considered with delayed channel state information at transmitters (CSIT). We propose a novel secure ret…