Secure Outsourcing of Multi-Armed Bandits
| Autor: | Marta Soare, Radu Ciucanu, Pascal Lafourcade, Marius Lombard-Platet |
|---|---|
| Přispěvatelé: | Ciucanu, Radu, Sécurité des Données et des Systèmes (SDS), Laboratoire d'Informatique Fondamentale d'Orléans (LIFO), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA), Laboratoire d'Informatique, de Modélisation et d'Optimisation des Systèmes (LIMOS), Ecole Nationale Supérieure des Mines de St Etienne (ENSM ST-ETIENNE)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Université Clermont Auvergne [2017-2020] (UCA [2017-2020]), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Be-Studys, Université d'Orléans (UO), Ecole Nationale Supérieure des Mines de St Etienne-Université Clermont Auvergne [2017-2020] (UCA [2017-2020])-Centre National de la Recherche Scientifique (CNRS), Département d'informatique de l'École normale supérieure (DI-ENS), École normale supérieure - Paris (ENS Paris), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université d'Orléans (UO), Ecole Nationale Supérieure des Mines de St Etienne-Centre National de la Recherche Scientifique (CNRS)-Université Clermont Auvergne [2017-2020] (UCA [2017-2020]) |
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
050101 languages & linguistics
Information privacy Theoretical computer science Computer science Markov process Cloud computing Cryptography 02 engineering and technology Outsourcing [INFO.INFO-CR]Computer Science [cs]/Cryptography and Security [cs.CR] symbols.namesake [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] 0202 electrical engineering electronic engineering information engineering [INFO.INFO-DB] Computer Science [cs]/Databases [cs.DB] 0501 psychology and cognitive sciences [INFO.INFO-CR] Computer Science [cs]/Cryptography and Security [cs.CR] [INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB] Cryptographic primitive business.industry 05 social sciences Maximization [INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] Cryptographic protocol symbols 020201 artificial intelligence & image processing business |
| Zdroj: | TrustCom 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020) 19th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom 2020), Dec 2020, Conférence online, China. pp.202-209 HAL |
| Popis: | International audience; We consider the problem of cumulative reward maximization in multi-armed bandits. We address the security concerns that occur when data and computations are outsourced to an honest-but-curious cloud i.e., that executes tasks dutifully, but tries to gain as much information as possible. We consider situations where data used in bandit algorithms is sensitive and has to be protected e.g., commercial or personal data. We rely on cryptographic schemes and propose UCB-DS, a distributed and secure protocol based on the UCB algorithm. We prove that UCB-DS computes the same cumulative reward as UCB while satisfying desirable security properties. In particular, cloud nodes cannot learn the cumulative reward or the sum of rewards for more than one arm. Moreover, by analyzing messages exchanged among cloud nodes, an external observer cannot learn the cumulative reward or the sum of rewards produced by some arm. We show that the overhead due to cryptographic primitives is linear in the size of the input. Our implementation confirms the linear-time behavior and the practical feasibility of our protocol, on both synthetic and real-world data.TR at https://sancy.iut-clermont.uca.fr/~lafourcade/PAPERS/PDF/CLLS2020.pdf |
| Databáze: | OpenAIRE |
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