Philip Derbeko

Philip Derbeko, Shlomi Dolev

The structure and weights of Deep Neural Networks (DNN) typically encode and contain very valuable information about the dataset that was used to train the network. One way to protect this information when DNN is published is to perform an interference of the network using secure multi-party computations (MPC). In this paper, we suggest a translation of deep neural networks to polynomials, which are easier to calculate efficiently with MPC techniques. We show a way to translate complete networks into a single polynomial and how to calculate the polynomial with an efficient and information-secure MPC algorithm. The calculation is done without intermediate communication between the participating parties, which is beneficial in several cases, as explained in the paper.

Philip Derbeko, Shlomi Dolev, Ehud Gudes et al.

MapReduce is a programming system for distributed processing large-scale data in an efficient and fault tolerant manner on a private, public, or hybrid cloud. MapReduce is extensively used daily around the world as an efficient distributed computation tool for a large class of problems, e.g., search, clustering, log analysis, different types of join operations, matrix multiplication, pattern matching, and analysis of social networks. Security and privacy of data and MapReduce computations are essential concerns when a MapReduce computation is executed in public or hybrid clouds. In order to execute a MapReduce job in public and hybrid clouds, authentication of mappers-reducers, confidentiality of data-computations, integrity of data-computations, and correctness-freshness of the outputs are required. Satisfying these requirements shield the operation from several types of attacks on data and MapReduce computations. In this paper, we investigate and discuss security and privacy challenges and requirements, considering a variety of adversarial capabilities, and characteristics in the scope of MapReduce. We also provide a review of existing security and privacy protocols for MapReduce and discuss their overhead issues.