000229308 001__ 229308
000229308 005__ 20190909115628.0
000229308 037__ $$aCONF
000229308 245__ $$aUnLynx: A Decentralized System for Privacy-Conscious Data Sharing
000229308 269__ $$a2017
000229308 260__ $$c2017
000229308 336__ $$aConference Papers
000229308 520__ $$aCurrent solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacypreserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.
000229308 6531_ $$aData sharing
000229308 6531_ $$aDecentralized system
000229308 6531_ $$aPrivacy
000229308 700__ $$g201807$$aFroelicher, David$$0249998
000229308 700__ $$g184951$$aEgger, Patricia$$0249940
000229308 700__ $$aSousa, João Sá
000229308 700__ $$g222090$$aRaisaro, Jean Louis$$0246661
000229308 700__ $$g221272$$aHuang, Zhicong$$0247593
000229308 700__ $$g217453$$aMouchet, Christian Vincent$$0251130
000229308 700__ $$aFord, Bryan
000229308 700__ $$g105427$$aHubaux, Jean-Pierre$$0240456
000229308 7112_ $$dJuly 18–21, 2017$$cMinneapolis, MN, USA$$aPrivacy Enhancing Technologies Symposium
000229308 773__ $$q152-170$$j4$$tProceedings on Privacy Enhancing Technologies
000229308 85641 $$yImplementation$$uhttps://github.com/lca1/unlynx
000229308 85641 $$yPaper$$uhttps://petsymposium.org/2017/papers/issue4/paper54-2017-4-source.pdf
000229308 8560_ $$fdavid.froelicher@epfl.ch
000229308 909C0 $$xU10426$$pLDS$$0252452
000229308 909CO $$qIC$$qGLOBAL_SET$$pconf$$ooai:infoscience.tind.io:229308
000229308 917Z8 $$x221272
000229308 917Z8 $$x221272
000229308 917Z8 $$x221272
000229308 937__ $$aEPFL-CONF-229308
000229308 973__ $$rREVIEWED$$aEPFL
000229308 980__ $$aCONF