Pushing the Limits of Encrypted Databases with Secure Hardware

Autor: Antonopoulos, Panagiotis, Arasu, Arvind, Eguro, Ken, Hammer, Joachim, Kaushik, Raghav, Kossmann, Donald, Ramamurthy, Ravi, Szymaszek, Jakub

Encrypted databases have been studied for more than 10 years and are quickly emerging as a critical technology for the cloud. The current state of the art is to use property-preserving encrypting techniques (e.g., deterministic encryption) to protect

Externí odkaz: http://arxiv.org/abs/1809.02631

A Learning Framework for Self-Tuning Histograms

Autor: Viswanathan, Raajay, Jain, Prateek, Laxman, Srivatsan, Arasu, Arvind

In this paper, we consider the problem of estimating self-tuning histograms using query workloads. To this end, we propose a general learning theoretic formulation. Specifically, we use query feedback from a workload as training data to estimate a hi

Externí odkaz: http://arxiv.org/abs/1111.7295

FastVer2: A Provably Correct Monitor for Concurrent, Key-Value Stores

Autor: Arasu, Arvind, Ramananandro, Tahina, Rastogi, Aseem, Swamy, Nikhil, Fromherz, Aymeric, Hietala, Kesha, Parno, Bryan, Ramamurthy, Ravi

Publikováno v: CPP '23-12th ACM SIGPLAN International Conference on Certified Programs and Proofs
CPP '23-12th ACM SIGPLAN International Conference on Certified Programs and Proofs, Jan 2023, Boston (MA), United States. pp.30-46, ⟨10.1145/3573105.3575687⟩

International audience; FastVer is a protocol that uses a variety of memory-checking techniques to monitor the integrity of key-value stores with only a modest runtime cost. Arasu et al. formalize the high-level design of FastVer in the F* proof assi

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f06896cf4c5114c147945b5c7c2f949
https://doi.org/10.1145/3573105.3575687

Characterizing Memory Requirements for Queries Over Continuous Data Streams.

Autor: Arasu, Arvind¹ arvinda@cs.stanford.edu, Babcock, Brian¹, Babu, Shivnath¹, McAlister, Jon¹, Widom, Jennifer¹

Publikováno v: ACM Transactions on Database Systems. Mar2004, Vol. 29 Issue 1, p162-194. 33p.

STREAM: The Stanford Data Stream Management System.

Autor: Arasu, Arvind, Babcock, Brian, Babu, Shivnath, Cieslewicz, John, Datar, Mayur, Ito, Keith, Motwani, Rajeev, Srivastava, Utkarsh, Widom, Jennifer

Publikováno v: Data Stream Management: Processing High-Speed Data Streams; 2016, p317-336, 20p