2012
Authors
Paulo, J; Reis, P; Pereira, J; Sousa, A;
Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Abstract
Deduplication is widely accepted as an effective technique for eliminating duplicated data in backup and archival systems. Nowadays, deduplication is also becoming appealing in cloud computing, where large-scale virtualized storage infrastructures hold huge data volumes with a significant share of duplicated content. There have thus been several proposals for embedding deduplication in storage appliances and file systems, providing different performance trade-offs while targeting both user and application data, as well as virtual machine images. It is however hard to determine to what extent is deduplication useful in a particular setting and what technique will provide the best results. In fact, existing disk I/O micro-benchmarks are not designed for evaluating deduplication systems, following simplistic approaches for generating data written that lead to unrealistic amounts of duplicates. We address this with DEDISbench, a novel micro-benchmark for evaluating disk I/O performance of block based deduplication systems. As the main contribution, we introduce the generation of a realistic duplicate distribution based on real datasets. Moreover, DEDISbench also allows simulating access hotspots and different load intensities for I/O operations. The usefulness of DEDISbench is shown by comparing it with Bonnie++ and IOzone open-source disk I/O micro-benchmarks on assessing two open-source deduplication systems, Opendedup and Lessfs, using Ext4 as a baseline. As a secondary contribution, our results lead to novel insight on the performance of these file systems. © 2012 Springer-Verlag.
2023
Authors
Brito, C; Ferreira, P; Portela, B; Oliveira, R; Paulo, J;
Publication
38TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, SAC 2023
Abstract
We propose Soteria, a system for distributed privacy-preserving Machine Learning (ML) that leverages Trusted Execution Environments (e.g. Intel SGX) to run code in isolated containers (enclaves). Unlike previous work, where all ML-related computation is performed at trusted enclaves, we introduce a hybrid scheme, combining computation done inside and outside these enclaves. The conducted experimental evaluation validates that our approach reduces the runtime of ML algorithms by up to 41%, when compared to previous related work. Our protocol is accompanied by a security proof, as well as a discussion regarding resilience against a wide spectrum of ML attacks.
2023
Authors
Esteves, T; Macedo, R; Oliveira, R; Paulo, J;
Publication
CoRR
Abstract
2021
Authors
Esteves, T; Miranda, M; Paulo, J; Portela, B;
Publication
IACR Cryptol. ePrint Arch.
Abstract
2021
Authors
Brito, C; Ferreira, P; Portela, B; Oliveira, R; Paulo, J;
Publication
IACR Cryptol. ePrint Arch.
Abstract
2023
Authors
Macedo, R; Miranda, M; Tanimura, Y; Haga, J; Ruhela, A; Harrell, SL; Evans, RT; Pereira, J; Paulo, J;
Publication
CoRR
Abstract
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