PPT-Secure Computation

Lecture 1 Arpita Patra Welcome to an exciting Journey gtgt Course Homepage http dronacsaiiscernetin arpita SecureComputation15 html gtgt References. : A Programming Language for Generic, Mixed-Mode Multiparty Computations . Aseem Rastogi. Matthew Hammer, Michael Hicks. To Appear in IEEE S&P 2014 (Oakland). What is Secure Computation. . A. . (. Lecture 3 & 4). Arpita. . Patra. Recap . >> Why secure computation?. >> What is secure (multi-party) computation (MPC)?. >> Secret Sharing and Secure sum protocol. >> OT and Secure multiplication protocol. 2.1TheArithmeticBlack-boxThearithmeticblack-boxallowsnparties,P1;:::;Pn,tosecurelystoreandretrieveelementsofaringZM.Here,MwillbeeitheraprimeoranRSA-modulus,i.e.theproductoftwooddprimes.Thesecurestorag (Lecture 2). Arpita. . Patra. Vishwaroop. of MPC. Expanding the scope of MPC. Dimension 1:. Any . polynomially. computable function can be computed securely.. >> So far you have seen how to compute addition and bit multiplication securely. Professor, Computer Science, UMD. Director, Maryland Cybersecurity Center. . Secure . Computation. Mathematics. Complexity . theory. C. ryptography. Cybersecurity. Science of . Cybersecurity. C. ryptography. Secure Multi-Party Computation. Debayan Gupta. Yale University. May 11, 2016. Jai . Dadabhai. The Apparent Paradox of . Modern Cryptography. Intuitive ideas of secrecy. Encrypted data are safe; but they appear to be unusable. Ranjit . Kumaresan. (MIT). Based on joint works with . Iddo. . Bentov. (. Technion. ), Tal Moran (IDC), Guy . Zyskind. (MIT). x. f. . (. x,y. ). y. f. . (. x,y. ). Secure Computation. Most general problem in cryptography. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. . Kartik . Nayak. With Xiao . Shaun . Wang, . Stratis. Ioannidis, Udi . Weinsberg. , Nina Taft, Elaine Shi. 1. 2. Users. Data. Data. Privacy concern!. Data Mining Engine. Data Model. Data Mining on User Data. Secure Multi-Party Computation. Debayan Gupta. Yale University. May 11, 2016. Jai . Dadabhai. The Apparent Paradox of . Modern Cryptography. Intuitive ideas of secrecy. Encrypted data are safe; but they appear to be unusable. Tore Frederiksen. , . Thomas . Jakobsen. , . Jesper. Nielsen, Peter . Nordholt. , Claudio . Orlandi. 27-08-2016. The LEGO Approach for. Maliciously Secure Two-Party Computation. 1. What we will present. Payman. . Mohassel. Yahoo Labs. 1. Do We Have the Same Person. in Mind?. Alice. Bob. Jack. . Joe. o. nly reveal . Yes/No. Solutions?. You have access to a trusted computer. You can use an airline reservation service. Chongwon Cho. . (HRL Laboratories). Sanjam. . Garg. (IBM T.J. Watson). . Rafail. . Ostrovsky. (UCLA). 2. Secure . Computation [Yao, GMW]. Alice and Bob. Alice holds input . x. .. Bob holds input . Arpita. . Patra. Recap . >Three orthogonal problems- (. n,t. )-sharing, reconstruction, multiplication protocol. > Verifiable Secret Sharing (VSS) will take care first two problems. >> .