Mahnush Movahedi

Brief announcement: breaking the O(nm) bit barrier, secure multiparty computation with a static adversary

We describe scalable algorithms for secure multiparty computation (SMPC). We assume a synchronous message passing communication model, but we do not assume the existence of a broadcast channel. Our main result holds for the case where there are <i>n</i> players, of which a 1/3-ε fraction are controlled by an adversary, for ε any positive constant. We describe an SMPC algorithm for this model that requires each player to send Õ(⁄<i>n</i>+<i>m</i><i>n</i> + √<i>n</i>) messages and perform Õ(⁄<i>n</i>+<i>m</i><i>n</i> + √<i>n</i>) computations to compute any function <i>f</i>, where <i>m</i> is the size of a circuit to compute <i>f</i>. We also consider a model where all players are rational. In this model, we describe a Nash equilibrium protocol that solves SMPC and requires each player to send Õ(⁄<i>n</i>+<i>m</i><i>n</i>) messages and perform Õ(⁄<i>n</i>+<i>mn</i>) computations. These results significantly improve over past results for SMPC which require each player to send a number of bits and perform a number of computations that is Θ(<i>n, m</i>)

Quorums Quicken Queries: Efficient Asynchronous Secure Multiparty Computation

We describe an asynchronous algorithm to solve secure multiparty computation MPC over n players, when strictly less than a

A DDoS-Aware IDS Model Based on Danger Theory and Mobile Agents

{1}\over{8}

Resource-Competitive Algorithms

fraction of the players are controlled by a static adversary. For any function f over a field that can be computed by a circuit with m gates, our algorithm requires each player to send a number of field elements and perform an amount of computation that is

Scalable rational secret sharing

\tilde{O}\frac{m}{n} + \sqrt n

Interactive Communication with Unknown Noise Rate

. This significantly improves over traditional algorithms, which require each player to both send a number of messages and perform computation that is Ωnm. Additionaly, we define the threshold counting problem and present a distributed algorithm to solve it in the asynchronous communication model. Our algorithm is load balanced, with computation, communication and latency complexity of Ologn, and may be of independent interest to other applications with a load balancing goal in mind.

Secure multi-party computation in large networks

Most of the security problems in large and distributed information systems are vastly complex. Few research on detection of burgeoning Distributed Denial of Service (DDoS) attacks, leads us to expand our perspectives to a comprehensive architectural approach in which particular issues like arrangement and communication of system components appear. In this paper, many useful ideas from the behaviour of biological immune systems are elicited based on the results of the latest immunological researches, especially Danger Theory. They are applied to create a model for immunization of distributed intrusion detection systems (IDSs) that are resistant to DDoS. Although a general model for various IDSs is proposed and implemented in this research, a particular simulation scenario for detecting DDoS in a wireless sensor network (WSN) is planned as an example to assess the general ability of the proposed model in detecting undesirable events.

Secure Multi-party Shuffling

The point of adversarial analysis is to model the worst-case performance of an algorithm. Unfortunately, this analysis may not always reect performance in practice because the adversarial assumption can be overly pessimistic. In such cases, several techniques have been developed to provide a more refined understanding of how an algorithm performs e.g., competitive analysis, parameterized analysis, and the theory of approximation algorithms. Here, we describe an analogous technique called resource competitiveness, tailored for distributed systems. Often there is an operational cost for adversarial behavior arising from bandwidth usage, computational power, energy limitations, etc. Modeling this cost provides some notion of how much disruption the adversary can inict on the system. In parameterizing by this cost, we can design an algorithm with the following guarantee: if the adversary pays T, then the additional cost of the algorithm is some function of T. Resource competitiveness yields results pertaining to secure, fault tolerant, and efficient distributed computation. We summarize these results and highlight future challenges where we expect this algorithmic tool to provide new insights.

Scalable mechanisms for rational secret sharing

We consider the classical secret sharing problem in the case where all agents are selfish but rational. In recent work, Kol and Naor show that in the non-simultaneous communciation model (i.e. when rushing is possible), there is no Nash equilibrium that ensures all agents learn the secret. However, they describe a mechanism for this problem that is an ε-Nash equilibrium, i.e. it is close to an equilibrium in the sense that no player can gain more than ε utility by deviating from it. Unfortunately, the Kol and Naor mechanism, and, to the best of our knowledge, all previous mechanisms for this problem require each agent to send O(n) messages in expectation, where n is the number of agents. This may be problematic for some applications of rational secret sharing such as secure multiparty computation and simulation of a mediator. We address this issue by describing a mechanism for rational n-out-of-n secret sharing that is an ε-Nash equilibrium, and is scalable in the sense that it requires each agent to send only an expected O(1) bits. Moreover, the latency of our mechanism is O(log n) in expectation, compared to O(n) expected latency for the Kol and Naor result. We also design mechanisms for a relaxed variant of rational m-out-of-n secret sharing where m = Θ(n) that require each processor to send O(log n) bits and have O(\log n) latency. Our mechanisms are non-cryptographic, and are not susceptible to backwards induction.

Secure location sharing

Alice and Bob want to run a protocol over a noisy channel, where a certain number of bits are flipped adversarially. Several results take a protocol requiring