Koti, Nishat ; Patra, Arpita ; Rachuri, Rahul ; Suresh, Ajith (2022)
Tetrad: Actively Secure 4PC for Secure Training and Inference.
29th Network and Distributed System Security Symposium (NDSS'22). San Diego, USA (24.04.2022 - 28.04.2022)
doi: 10.14722/ndss.2022.24058
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular. Towards this, we propose a framework for the case of four parties with at most one active corruption called Tetrad. Tetrad works over rings and supports two levels of security, fairness and robustness. The fair multiplication protocol costs 5 ring elements, improving over the state-of-the-art Trident (Chaudhari et al. NDSS’20). A key feature of Tetrad is that robustness comes for free over fair protocols. Other highlights across the two variants include (a) probabilistic truncation without overhead, (b) multi-input multiplication protocols, and (c) conversion protocols to switch between the computational domains, along with a tailor-made garbled circuit approach. Benchmarking of Tetrad for both training and inference is conducted over deep neural networks such as LeNet and VGG16. We found that Tetrad is up to 4 times faster in ML training and up to 5 times faster in ML inference. Tetrad is also lightweight in terms of deployment cost, costing up to 6 times less than Trident.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Koti, Nishat ; Patra, Arpita ; Rachuri, Rahul ; Suresh, Ajith |
| Art des Eintrags: | Bibliographie |
| Titel: | Tetrad: Actively Secure 4PC for Secure Training and Inference |
| Sprache: | Englisch |
| Publikationsjahr: | April 2022 |
| Buchtitel: | Network and Distributed Systems Security (NDSS) Symposium 2022 |
| Kollation: | 18 Seiten |
| Veranstaltungstitel: | 29th Network and Distributed System Security Symposium (NDSS'22) |
| Veranstaltungsort: | San Diego, USA |
| Veranstaltungsdatum: | 24.04.2022 - 28.04.2022 |
| DOI: | 10.14722/ndss.2022.24058 |
| URL / URN: | https://www.ndss-symposium.org/ndss-paper/auto-draft-202/ |
| Zugehörige Links: | |
| Kurzbeschreibung (Abstract): | Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular. Towards this, we propose a framework for the case of four parties with at most one active corruption called Tetrad. Tetrad works over rings and supports two levels of security, fairness and robustness. The fair multiplication protocol costs 5 ring elements, improving over the state-of-the-art Trident (Chaudhari et al. NDSS’20). A key feature of Tetrad is that robustness comes for free over fair protocols. Other highlights across the two variants include (a) probabilistic truncation without overhead, (b) multi-input multiplication protocols, and (c) conversion protocols to switch between the computational domains, along with a tailor-made garbled circuit approach. Benchmarking of Tetrad for both training and inference is conducted over deep neural networks such as LeNet and VGG16. We found that Tetrad is up to 4 times faster in ML training and up to 5 times faster in ML inference. Tetrad is also lightweight in terms of deployment cost, costing up to 6 times less than Trident. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Praktische Kryptographie und Privatheit DFG-Sonderforschungsbereiche (inkl. Transregio) DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 2050 Privacy and Trust for Mobile Users DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1119: CROSSING – Kryptographiebasierte Sicherheitslösungen als Grundlage für Vertrauen in heutigen und zukünftigen IT-Systemen |
| Hinterlegungsdatum: | 25 Jul 2024 07:17 |
| Letzte Änderung: | 25 Jul 2024 07:17 |
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