Results 1031 - 1040 of 24561

Statistical heterogeneity of data in FL has motivated the design of personalized learning, where individual (personalized) models are trained, through collaboration. We build on a statistical framework to propose adaptive methods called ADEPT, which balance local information and collaboration. We examine through this lens, personalized unsupervised learning tasks including diffusion based generative models. We also develop a different methodology for personalized diffusion models called SPIRE, which we show arises from a Gaussian mixture model heterogeneity. This also allows for lightweight adaptation for new users who did not participate in collaboration, supporting privacy through data minimization directly. We finally  focus on online learning, where we first present privacy for multi-arm bandit problems. Then we present an  instantiation of personalized online learning through multi-agent multi-armed bandit problems, where we demonstrate a complete characterization for regret of heterogeneous stochastic linear bandits. 

Parts of this work are joint with Kaan Ozkara, Ruida Zhou, Bruce Huang and Antonious Girgis.

In this work, we identify a set of side-channels in our Confidential Federated Compute platform that a hypothetical insider could exploit to circumvent differential privacy (DP) guarantees. We show how DP can mitigate two of the side-channels, one of which has been implemented in our open-source library.

When and how can we guarantee that the conclusions arrived at by a complicated and expensive data analysis are correct? A sequence of recent works explores the possibility of constructing interactive proof systems that can verify the conclusions using less data and computation than would be needed to replicate the analysis. I will survey this line of work, highlighting positive and negative results.

Based on joint works with Tal Herman.

LLM-based agents are inherently probabilistic and ill-suited for security-critical tasks, especially in applications handling sensitive data where privacy risks often arise after access is granted. We present AgentCrypt, a framework that prioritizes privacy over correctness by addressing post-access leakage through tool calls, memory, and derived outputs. AgentCrypt introduces a three-tier architecture for fine-grained, privacy-preserving multi-agent workflows and provides formal security guarantees for tagged data. It integrates seamlessly with existing platforms, demonstrated through implementations with LangGraph and Google ADK, while remaining platform-agnostic.

An end-to-end encrypted application needs a mechanism for backing up secret keys. Existing deployed systems create a single point of privacy failure: by compromising one secure hardware device, an attacker can recover many users’ secrets. In this talk, I will describe two architectures for encrypted backups that split secrets across different system components. Both architectures are motivated by deployment constraints. First, I will present one system that splits secrets across different types of enclaves run by different cloud providers (SVR3, OSDI’24). Then, I will discuss another system that splits secrets across application clients and offloads work, but not secrets, to the application server (Chorus, IEEE S&P’26). This talk is based on joint work with Graeme Connell, Vivian Fang, Allison Li, Raluca Ada Popa, Deevashwer Rathee, and Rolfe Schmidt.

The Distributed Aggregation Protocol is a standard for privately computing statistical aggregations over measurements in multi-party computation being developed at the Internet Engineering Task Force. In this task, we'll cover what DAP is, how the network protocol layer interacts with the cryptography layer, some unexpected use cases that have emerged as well as challenges in implementing and deploying this technology. I submitted this abstract at the request of Henry Corrigan-Gibbs.

Secure Aggregation allows an untrusted server to compute aggregate statistics over large populations of users, without ever learning individual-level data. State-of-the-art secure aggregation protocols allow the vast majority of clients to send a single message, by either splitting the computation between two or more servers, or by outsourcing trust to a small committee of clients. This talk will cover the Willow secure aggregation protocol (Crypto 2025), its recent improvement WillowFold (ePrint 2026/264), and practical considerations when deploying secure aggregation at scale.

Large-scale online services increasingly rely on aggregating sensitive user data, for example to support analytics or federated learning. Private aggregation enables such computation without revealing any individual client’s input, but many established designs typically rely on distributing trust among a small set of non-colluding servers or auxiliary infrastructure, which complicates deployment. In this talk, I present a line of work that rethinks private aggregation through client-decentralized trust. The key insight is that, the large population of participating clients can be leveraged as a resource to decentralize trust, and enables aggregation with a single untrusted server. This design choice, however, introduces a central challenge: how to keep the clients lightweight enough for practical deployment on resource-constrained devices. I will describe two systems that embody this approach and address this challenge. Flamingo is a fast multi-round private aggregation system tailored to federated learning setting. Armadillo extends this line of work with robustness against disruptive clients in the system.