USENIX

Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Removing Obstacles before Breaking Through the Memory Wall: A Close Look at HBM Errors in the Field

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Half-Double: Hammering From the Next Row Over

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Throwhammer: Rowhammer Attacks over the Network and Defenses

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants

Large Language Models (LLMs) such as OpenAI Codex are increasingly being used as AI-based coding assistants. Understanding the impact of these tools on developers’ code is paramount, especially as ...
USENIX

HetPipe: Enabling Large DNN Training on (Whimpy) Heterogeneous GPU Clusters through Integration of Pipelined Model Parallelism and Data Parallelism

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

NXNSAttack: Recursive DNS Inefficiencies and Vulnerabilities

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

SP-PIFO: Approximating Push-In First-Out Behaviors using Strict-Priority Queues

USENIX is committed to Open Access to the research presented at our events. Papers and proceedings are freely available to everyone once the event begins. Any video, audio, and/or slides that are ...
USENIX

Systematic Evaluation of Privacy Risks of Machine Learning Models

Machine learning models are prone to memorizing sensitive data, making them vulnerable to membership inference attacks in which an adversary aims to guess if an input sample was used to train the ...
USENIX

Don’t be Dense: Efficient Keyword PIR for Sparse Databases

In this paper, we introduce SparsePIR, a single-server keyword private information retrieval (PIR) construction that enables querying over sparse databases. At its core, SparsePIR is based on a novel ...
USENIX

VoteAgain: A scalable coercion-resistant voting system

The strongest threat model for voting systems considers coercion resistance: protection against coercers that force voters to modify their votes, or to abstain. Existing remote voting systems either ...
USENIX

The Limitations of Federated Learning in Sybil Settings

Federated learning over distributed multi-party data is an emerging paradigm that iteratively aggregates updates from a group of devices to train a globally shared model. Relying on a set of devices, ...