Date & Time: Thursday, April 4, 2013       3:30 - 4:30 p.m.

Location: ITB A113A

Speaker: Dragan Rakas

Title: Fully Homomorphic Encryption and Encrypted Programming Languages

Abstract:
Fully homomorphic encryption allows computations to be made on encrypted data without decryption, while preserving data integrity. This feature is desirable in a variety of applications such as banking, search engine and database querying, and some cloud computing services. In this presentation, we discuss a method of encrypting circuits and executing encrypted assembly instructions, by combining fully homomorphic encryption and digital logic theory. We use the MIPS Assembly Architecture as a base for our work, and the result is essentially an encrypted programming language, where a remote server is capable of executing program code that was written and encrypted by a local client.

We would like to extend an invitation to interested FRAISE Research Group members to attend an upcoming McMaster University Advanced Optimization Lab (AdvOL) seminar. The details of the seminar are given below:

Date & Time: Thursday, March 28, 2013       4:30 - 5:30 p.m.

Location: ITB 201

Speaker: Xiao-Wen Chang
                School of Computer Science
                McGill University

Title: Effectiveness of the LLL Lattice Reduction in Solving Integer Least Squares Problems

Abstract:
In some applications such as GPS and wireless communications, there is a linear model, which involves an unknown integer parameter vector. The common method to estimate the integer parameter vector is to solve an integer least squares (ILS) problem, also referred to as a closest vector problem. A typical approach to solving an ILS problem is the so called sphere decoding, a discrete search method. To make a sphere decoder faster, the well-known Lenstra, Lenstra and Lovasz (LLL) lattice reduction is often used as preprocessing. As a general ILS problem is NP-hard, for some applications, an approximate solution, which can be produced quickly, is computed instead. One often used approximate solution is the Babai point, the first integer point found by a typical sphere decoder. In order to verify whether an estimator is good enough for a practical use, one needs to find the probability of the estimator being equal to the true integer parameter vector, which is referred to as success probability. In addition to making the search process faster, it has been observed that the LLL reduction can also improve the success probability of the Babai point. But there has been no rigorous theory about either observation so far. In this talk we will show rigorously in theory why both are true.

This is joint work with Jinming Wen and Xiaohu Xie.