Computer Science and Artificial Intelligence Lab (CSAIL)
http://hdl.handle.net/1721.1/5458
2017-11-22T18:20:46ZOn the Non-Existence of Blockwise 2-Local PRGs with Applications to Indistinguishability Obfuscation
http://hdl.handle.net/1721.1/107928
On the Non-Existence of Blockwise 2-Local PRGs with Applications to Indistinguishability Obfuscation
Lombardi, Alex; Vaikuntanathan, Vinod
Lin and Tessaro (Eprint 2017/250) recently proposed indistinguishability obfuscation and functional encryption candidates and proved their security based on a standard assumption on bilinear maps and a non-standard assumption on ``Goldreich-like'' pseudorandom generators (PRG). In a nutshell, they require the existence of pseudo-random generators $G:\Sigma^n \to \{0,1\}^m$ for some $\mathsf{poly}(n)$-size alphabet $\Sigma$ where each output bit depends on at most two input alphabet symbols, and which achieve sufficiently large stretch. We show a polynomial-time attack against such generators. Our attack uses tools from the literature on two-source extractors (Chor and Goldreich, SICOMP 1988) and efficient refutation of 2-CSPs over large alphabets (Allen, O'Donnell and Witmer, FOCS 2015). Finally, we propose new ways to instantiate the Lin-Tessaro construction that do not immediately fall to our attacks. While we cannot say with any confidence that these modifications are secure, they certainly deserve further cryptanalysis.
2017-04-06T00:00:00ZOptimal and Player-Replaceable Consensus with an Honest Majority
http://hdl.handle.net/1721.1/107927
Optimal and Player-Replaceable Consensus with an Honest Majority
Micali, Silvio; Vaikuntanathan, Vinod
We construct a Byzantine Agreement protocol that tolerates t < n/2 corruptions, is very efficient in terms of the number of rounds and the number of bits of communication, and satisfies a strong notion of robustness called player replaceability (defined in [Mic16]). We provide an analysis of our protocol when executed on real-world networks such as the ones employed in the bitcoin protocol.
2017-03-31T00:00:00ZThe Tensor Algebra Compiler
http://hdl.handle.net/1721.1/107013
The Tensor Algebra Compiler
Kjolstad, Fredrik; Kamil, Shoaib; Chou, Stephen; Lugato, David; Amarasinghe, Saman
Tensor and linear algebra is pervasive in data analytics and the physical sciences. Often the tensors, matrices or even vectors are sparse. Computing expressions involving a mix of sparse and dense tensors, matrices and vectors requires writing kernels for every operation and combination of formats of interest. The number of possibilities is infinite, which makes it impossible to write library code for all. This problem cries out for a compiler approach. This paper presents a new technique that compiles compound tensor algebra expressions combined with descriptions of tensor formats into efficient loops. The technique is evaluated in a prototype compiler called taco, demonstrating competitive performance to best-in-class hand-written codes for tensor and matrix operations.
2017-02-17T00:00:00ZCollaborative Diagnosis of Over-Subscribed Temporal Plans
http://hdl.handle.net/1721.1/106886
Collaborative Diagnosis of Over-Subscribed Temporal Plans
Yu, Peng
Over-subscription, that is, being assigned too many tasks or requirements that are too demanding, is commonly encountered in temporal planning problems. As human beings, we often want to do more than we can, ask for things that may not be available, while underestimating how long it takes to perform each task. It is often difficult for us to detect the causes of failure in such situations and then find resolutions that are effective. We can greatly benefit from tools that assist us by looking out for these plan failures, by identifying their root causes, and by proposing preferred resolutions to these failures that lead to feasible plans. In recent literature, several approaches have been developed to resolve such over-subscribed problems, which are often framed as over-constrained scheduling, configuration design or optimal planning problems. Most of them take an all-or-nothing approach, in which over-subscription is resolved through suspending constraints or dropping goals. While helpful, in real-world scenarios, we often want to preserve our plan goals as much possible. As human beings, we know that slightly weakening the requirements of a travel plan, or replacing one of its destinations with an alternative one is often sufficient to resolve an over-subscription problem, no matter if the requirement being weakened is the duration of a deep-sea survey being planned for, or the restaurant cuisine for a dinner date. The goal of this thesis is to develop domain independent relaxation algorithms that perform this type of slight weakening of constraints, which we will formalize as continuous relaxation, and to embody them in a computational aid, Uhura, that performs tasks akin to an experienced travel agent or ocean scientists. In over-subscribed situations, Uhura helps us diagnose the causes of failure, suggests alternative plans, and collaborates with us in order to resolve conflicting requirements in the most preferred way. Most importantly, the algorithms underlying Uhura supports the weakening, instead of suspending, of constraints and variable domains in a temporally flexible plan. The contribution of this thesis is two-fold. First, we developed an algorithmic framework, called Best-first Conflict-Directed Relaxation (BCDR), for performing plan relaxation. Second, we use the BCDR framework to perform relaxation for several different families of plan representations involving different types of constraints. These include temporal constraints, chance constraints and variable domain constraints, and we incorporate several specialized conflict detection and resolution algorithms in support of the continuous weakening of them. The key idea behind BCDR's approach to continuous relaxation is to generalize the concepts of discrete conflicts and relaxations, first introduced by the model-based diagnosis community, to hybrid conflicts and relaxations, which denote minimal inconsistencies and minimal relaxations to both discrete and continuous relaxable constraints.
PhD thesis
2016-10-14T00:00:00Z