2 citations · 3 across the 21 of their papers we have counts for
22 papers
Feedback Coding Enables Inference-Time Covert Agentic Communication
Sidong Guo, Sajani Vithana, Atefeh Gilani +3
As large language models (LLMs) are increasingly used to automate digital interactions, users can leverage LLM-generated text as cover for covert communication within seemingly ben…
Optimal Domain-Aware Privacy Mechanisms for Synthetic Data Generation
Sajani Vithana, Sangwon Jung, Haoyang Hu +3
Differential privacy (DP) imposes fundamental trade-offs between privacy and statistical fidelity in synthetic data generation. While access to public data has been shown to improv…
ArcMark: Distortion-Free Multi-Byte LLM Watermark via Optimal Transport
Atefeh Gilani, Sajani Vithana, Carol Xuan Long +3
Watermarking is an important tool for promoting the responsible use of large language models (LLMs). Existing watermarks insert a signal into generated tokens that either flags LLM…
HeavyWater and SimplexWater: Distortion-Free LLM Watermarks for Low-Entropy Next-Token Predictions
Dor Tsur, Carol Xuan Long, Claudio Mayrink Verdun +5
Large language model (LLM) watermarks enable authentication of text provenance, curb misuse of machine-generated text, and promote trust in AI systems. Current watermarks operate b…
Multi-Group Proportional Representation for Text-to-Image Models
Sangwon Jung, Alex Oesterling, Claudio Mayrink Verdun +3
Text-to-image (T2I) generative models can create vivid, realistic images from textual descriptions. As these models proliferate, they expose new concerns about their ability to rep…
Multi-Group Proportional Representation in Retrieval
Alex Oesterling, Claudio Mayrink Verdun, Carol Xuan Long +5
Image search and retrieval tasks can perpetuate harmful stereotypes, erase cultural identities, and amplify social disparities. Current approaches to mitigate these representationa…
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
Sajani Vithana, Viveck R. Cadambe, Flavio P. Calmon +1
Differentially private distributed mean estimation (DP-DME) is a fundamental building block in privacy-preserving federated learning, where a central server estimates the mean of $…
Private Membership Aggregation
Mohamed Nomeir, Sajani Vithana, Sennur Ulukus
We consider the problem of private membership aggregation (PMA), in which a user counts the number of times a certain element is stored in a system of independent parties that stor…
Quantum Symmetric Private Information Retrieval with Secure Storage and Eavesdroppers
Alptug Aytekin, Mohamed Nomeir, Sajani Vithana +1
We consider both the classical and quantum variations of -secure, -eavesdropped and -colluding symmetric private information retrieval (SPIR). This is the first work to st…
Information-Theoretically Private Federated Submodel Learning with Storage Constrained Databases
Sajani Vithana, Sennur Ulukus
In federated submodel learning (FSL), a machine learning model is divided into multiple submodels based on different types of data used for training. Each user involved in the trai…
Deceptive Information Retrieval
Sajani Vithana, Sennur Ulukus
We introduce the problem of deceptive information retrieval (DIR), in which a user wishes to download a required file out of multiple independent files stored in a system of databa…
Asymmetric -Secure -Private Information Retrieval: More Databases is Not Always Better
Mohamed Nomeir, Sajani Vithana, Sennur Ulukus
We consider a special case of -secure -private information retrieval (XSTPIR), where the security requirement is \emph{asymmetric} due to possible missing communication links…
Private Information Retrieval and Its Applications: An Introduction, Open Problems, Future Directions
Sajani Vithana, Zhusheng Wang, Sennur Ulukus
Private information retrieval (PIR) is a privacy setting that allows a user to download a required message from a set of messages stored in a system of databases without revealing…
Private Read-Update-Write with Controllable Information Leakage for Storage-Efficient Federated Learning with Top Sparsification
Sajani Vithana, Sennur Ulukus
In federated learning (FL), a machine learning (ML) model is collectively trained by a large number of users, using their private data in their local devices. With top sparsifi…
Private Read Update Write (PRUW) With Heterogeneous Databases
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) with heterogeneous storage constrained databases in federated submodel learning (FSL). In FSL a machine learning (ML)…
Model Segmentation for Storage Efficient Private Federated Learning with Top Sparsification
Sajani Vithana, Sennur Ulukus
In federated learning (FL) with top sparsification, millions of users collectively train a machine learning (ML) model locally, using their personal data by only communicating…
Rate-Privacy-Storage Tradeoff in Federated Learning with Top Sparsification
Sajani Vithana, Sennur Ulukus
We investigate the trade-off between rate, privacy and storage in federated learning (FL) with top sparsification, where the users and the servers in the FL system only share t…
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) in relation to private federated submodel learning (FSL), where a machine learning model is divided into multiple sub…
Rate Distortion Tradeoff in Private Read Update Write in Federated Submodel Learning
Sajani Vithana, Sennur Ulukus
We investigate the rate distortion tradeoff in private read update write (PRUW) in relation to federated submodel learning (FSL). In FSL a machine learning (ML) model is divided in…
Private Federated Submodel Learning with Sparsification
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) in federated submodel learning (FSL) with sparsification. In FSL, a machine learning model is divided into multiple s…
Private Read Update Write (PRUW) with Storage Constrained Databases
Sajani Vithana, Sennur Ulukus
We investigate the problem of private read update write (PRUW) in relation to federated submodel learning (FSL) with storage constrained databases. In PRUW, a user privately reads…
Semantic Private Information Retrieval
Sajani Vithana, Karim Banawan, Sennur Ulukus
We investigate the problem of semantic private information retrieval (semantic PIR). In semantic PIR, a user retrieves a message out of independent messages stored in repli…