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Two-Party Decision Tree Training from Updatable Order-Revealing Encryption

Berger, Robin ORCID iD icon 1; Dörre, Felix ORCID iD icon 1; Koch, Alexander
1 Karlsruher Institut für Technologie (KIT)

Abstract:

Running machine learning algorithms on encrypted data is a way forward to marry functionality needs common in industry with the important concerns for privacy when working with potentially sensitive data. While there is already a variety of protocols in this setting based on fully homomorphic encryption or secure multiparty computation (MPC), we are the first to propose a protocol that makes use of a specialized Order-Revealing Encryption scheme. This scheme allows to do secure comparisons on ciphertexts and update these ciphertexts to be encryptions of the same plaintexts but under a new key. We call this notion Updatable Order-Revealing Encryption (uORE) and provide a secure construction using a key-homomorphic pseudorandom function.

In a second step, we use this scheme to construct an efficient three-round protocol between two parties to compute a decision tree (or forest) on labeled data provided by both parties. The protocol is in the passively-secure setting and has some leakage on the data that arises from the comparison function on the ciphertexts. We motivate how our protocol can be compiled into an actively-secure protocol with less leakage using secure enclaves, in a graceful degradation manner, e.g. ... mehr


Postprint §
DOI: 10.5445/IR/1000169877/post
Veröffentlicht am 30.04.2024
Preprint §
DOI: 10.5445/IR/1000169877
Veröffentlicht am 06.05.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Informationssicherheit und Verlässlichkeit (KASTEL)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2024
Sprache Englisch
Identifikator ISBN: 978-3-031-54769-0
ISSN: 0302-9743
KITopen-ID: 1000169877
HGF-Programm 46.23.01 (POF IV, LK 01) Methods for Engineering Secure Systems
Erschienen in Applied Cryptography and Network Security : 22nd International Conference, ACNS 2024, Abu Dhabi, United Arab Emirates, March 5-8, 2024, Proceedings. Part I. Ed.: C. Pöpper
Veranstaltung 22nd International Conference on Applied Crypthography and Network Security (ACNS 2024), Abu Dhabi, Vereinigte Arabische Emirate, 05.03.2024 – 08.03.2024
Verlag Springer International Publishing
Seiten 288–317
Serie Lecture Notes in Computer Science ; 14583
Vorab online veröffentlicht am 01.03.2024
Schlagwörter Secure Computation, Order-Revealing Encryption, Decision Tree, Learning, Enclaves, Privacy-Preserving Machine Learning
Nachgewiesen in Dimensions
Scopus
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