Graphiti: Secure Graph Computation Made More Scalable
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CDPG
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January 10, 2025
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Data Privacy
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December 2024 Graphiti: Secure Graph Computation Made More Scalable Authors: Koti, N., Kukkala, V. B., Patra, A., & Raj Gopal, B. Privacy-preserving graph analysis allows performing computations on graphs that store sensitive information, while ensuring all the information about the topology of the graph as well as data associated with the nodes and edges remains hidden. The current work addresses …
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Ruffle: Rapid 3-party shuffle protocols
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CDPG
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January 10, 2025
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Data Privacy
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March 2023 Ruffle: Rapid 3-party shuffle protocols Authors: Koti, N., Kukkala, V. B., Patra, A., Gopal, B. R., & Sangal, S Secure shuffle is an important primitive that finds use in several applications such as secure electronic voting, oblivious RAMs, secure sorting, to name a few. For time-sensitive shuffle-based applications that demand a fast response time, it is essential to …
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Vogue: Faster computation of private heavy hitters
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CDPG
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January 10, 2025
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Data Privacy
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October 2023 Vogue: Faster computation of private heavy hitters Authors: Jangir, P., Koti, N., Kukkala, V. B., Patra, A., Gopal, B. R., & Sangal, S Consider the problem of securely identifying τ -heavy hitters, where given a set of client inputs, the goal is to identify those inputs which are held by at least τ clients in a privacy-preserving manner. …
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Shield: Secure Allegation Escrow System with Stronger Guarantees
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CDPG
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January 9, 2025
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Data Privacy
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April 2023 Shield: Secure Allegation Escrow System with Stronger Guarantees Authors: Koti, N., Kukkala, V. B., Patra, A., & Gopal, B. R The rising issues of harassment, exploitation, corruption, and other forms of abuse have led victims to seek comfort by acting in unison against common perpetrators (e.g., #MeToo movement). One way to curb these issues is to install allegation …
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Find thy neighbourhood: Privacy-preserving local clustering.
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CDPG
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January 9, 2025
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Data Privacy
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December 2022 Find thy neighbourhood: Privacy-preserving local clustering Authors: Koti, Nishat, Varsha Bhat Kukkala, Arpita Patra, and Bhavish Raj Gopal Identifying a cluster around a seed node in a graph, termed local clustering, finds use in several applications, including fraud detection, targeted advertising, community detection, etc. However, performing local clustering is challenging when the graph is distributed among multiple data …
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Pentagod: Stepping beyond traditional god with five parties
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CDPG
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January 9, 2025
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Data Privacy
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Aug 2022 Pentagod: Stepping beyond traditional god with five parties Authors: Koti, N., Kukkala, V. B., Patra, A., & Raj Gopal, B. Secure multiparty computation (MPC) is increasingly being used to address privacy issues in various applications. The recent work of Alon et al. (CRYPTO’20) identified the shortcomings of traditional MPC and defined a Friends-and-Foes (FaF) security notion to address …
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Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators
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CDPG
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January 9, 2025
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Data Privacy
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December 2023 Performance Characterization of Containerized DNN Training and Inference on Edge Accelerators Authors: Prashanthi S.K., Vinayaka Hegde, Keerthana Patchava, Ankita Das and Yogesh Simmhan Edge devices have typically been used for DNN in-ferencing. The increase in the compute power of accelerated edges is leading to their use in DNN training also. As privacy becomes a concern on multi-tenant edge …
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Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks
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CDPG
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January 9, 2025
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Data Privacy
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Aug 2024 Optimizing Federated Learning using Remote Embeddings for Graph Neural Networks Authors: Pranjal Naman and Yogesh Simmhan, Graph Neural Networks (GNNs) have experienced rapid advancements in recent years due to their ability to learn meaningful representations from graph data structures. Federated Learning (FL) has emerged as a viable machine learning approach for training a shared model on decentralized data, addressing privacy concerns while leveraging …
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Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning
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CDPG
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January 9, 2025
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Data Privacy
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April 2024 Minimizing Layerwise Activation Norm Improves Generalization in Federated Learning Authors: M. Yashwanth, G. K. Nayak, H. Rangwani, A. Singh, R. V. Babu, A. Chakraborty Federated Learning (FL) is an emerging machine learning framework that enables multiple clients (coordinated by a server) to collaboratively train a global model by aggregating the locally trained models without sharing any client’s training data. …
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Continual Mean Estimation Under User-Level Privacy
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CDPG
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January 9, 2025
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Data Privacy
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December 2022 Continual Mean Estimation Under User-Level Privacy Authors: A. J. George, L. Ramesh, A. V. Singh and H. Tyagi We consider the problem of continually releasing an estimate of the population mean of a stream of samples that is user-level differentially private (DP). At each time instant, a user contributes a sample, and the users can arrive in arbitrary …
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