Synthesis of a simple self-stabilizing system

With the increasing importance of distributed systems as a computing paradigm, a systematic approach to their design is needed. Although the area of formal verification has made enormous advances towards this goal, the resulting functionalities are limited to detecting problems in a particular design. By means of a classical example, we illustrate a simple template-based approach to computer-aided design of distributed systems based on leveraging the well-known technique of bounded model checking to the synthesis setting.Comment: In Proceedings SYNT 2014, arXiv:1407.493

Unification on Compressed Terms

First-order term unification is an essential concept in areas like functional and logic programming, automated deduction, deductive databases, artificial intelligence, information retrieval, compiler design, etc. We build upon recent developments in grammar-based compression mechanisms for terms and investigate algorithms for first-order unification and matching on compressed terms. We prove that the first-order unification of compressed terms is decidable in polynomial time, and also that a compressed representation of the most general unifier can be computed in polynomial time. Furthermore, we present a polynomial time algorithm for first-order matching on compressed terms. Both algorithms represent an improvement in time complexity over previous results [GGSS09, GGSS08]. We use several known results on the tree grammars used for compression, called singleton tree grammars (STG)s, like polynomial time computability of several subalgorithmms: certain grammar extensions, deciding equality of represented terms, and generating their preorder traversal. An innovation is a specialized depth of an STG that shows that unifiers can be represented in polynomial spac

Unification and Matching on Compressed Terms

Term unification plays an important role in many areas of computer science, especially in those related to logic. The universal mechanism of grammar-based compression for terms, in particular the so-called Singleton Tree Grammars (STG), have recently drawn considerable attention. Using STGs, terms of exponential size and height can be represented in linear space. Furthermore, the term representation by directed acyclic graphs (dags) can be efficiently simulated. The present paper is the result of an investigation on term unification and matching when the terms given as input are represented using different compression mechanisms for terms such as dags and Singleton Tree Grammars. We describe a polynomial time algorithm for context matching with dags, when the number of different context variables is fixed for the problem. For the same problem, NP-completeness is obtained when the terms are represented using the more general formalism of Singleton Tree Grammars. For first-order unification and matching polynomial time algorithms are presented, each of them improving previous results for those problems.Comment: This paper is posted at the Computing Research Repository (CoRR) as part of the process of submission to the journal ACM Transactions on Computational Logic (TOCL)

Variants of unification considering compression and context variables

Term unification is a basic operation in several areas of computer science, specially in those related to logic. Generally speaking, it consists on solving equations over expressions called terms. Depending on the kind of variables allowed to occur in the terms and under which conditions two terms are considered to be equal, several frameworks of unification such as first-order unification, higher-order unification, syntactic unification, and unification modulo theories can be distinguished. Moreover, other variants of term unification arise when we consider nontrivial representations for terms. In this thesis we study variants of the classic first-order syntactic term unification problem resulting from the introduction of context variables, i.e. variables standing for contexts, and/or assuming that the input is given in some kind of compressed representation. More specifically, we focus on two of such compressed representations: the well-known Directed Acyclic Graphs (DAGs) and Singleton Tree Grammars (STGs). Similarly as DAGs allow compression by exploiting the reusement of repeated instances of a subterm in a term, STGs are a grammar-based compression mechanism based on the reusement of repeated (multi)contexts. An interesting property of the STG formalism is that many operations on terms can be efficiently performed directly in their compressed representation thus taking advantage of the compression also when computing with the represented terms. Although finding a minimal STG representation of a term is computationally difficult, this limitation has been successfully overcome is practice, and several STG-compressors for terms are available. The STG representation has been applied in XML processing and analysis of Term Rewrite Systems. Moreover, STGs are a very useful concept for the analysis of unification problems since, roughly speaking, allow to represent solutions in a succinct but still efficiently verifiable form.La unificació de termes és una operación básica en diverses àrees de la informática, especialment en aquelles relacionades amb la lógica. En termes generals, consisteix en resoldre equacions sobre expressions anomenades termes. Depenent del tipus de variables que apareguin en els termes i de sota quines condicions dos termes són considerats iguals, podem definir diverses variants del problema d'unificació de termes. A més a més, altres variants del problema d'unificació de termes sorgeixen quan considerem representacions no trivials per als termes. S'estudia variants del problema clàssic d'unificació sintáctica de primer ordre resultants de la introducció de variables de context i/o de l'assumpció de que l'entrada és donada en format comprimit. Ens centrem en l'estudi de dues representacions comprimides: els grafs dirigits acíclics i les gramàtiques d'arbre singletó. Similarment a com els grafs dirigits acíclics permeten compressió mitjançant el reús d'instàncies repetides d'un mateix subterme, les gramàtiques d'arbres singletó són un sistema de compressió basat en el reús de (multi)contexts. Una propietat interessant d'aquest formalisme és que moltes de les operacions comuns sobre termes es poden realizar directament sobre la versió comprimida d'aquests de forma eficient, sense cap mena de descompressió prèvia. Tot i que trobar una representació minimal d'un terme amb una gramática singletó és una tasca computacionalment difícil, aquesta limitació ha estat resolta de forma satisfactòria en la pràctica, hi ha disponibles diversos compressors per a termes. La representació de termes amb gramàtiques singletó ha estat útil per al processament de documents XML i l'anàlisi de sistemes de reescriptura de termes. Les gramàtiques singletó són concepte molt útil per a l'anàlisi de problemas d'unificació, ja que permeten representar solucions de forma comprimida sense renunciar a que puguin ser verificades de forma eficient. A la primera part d'aquesta tesi s'estudien els problemas d'unificació i matching de primer ordre sota l'assumpció de que els termes de l'entrada són representats fent servir gramàtiques singletó. Presentem algorismes de temps polinòmic per a aquests problemas, així com propostes d'implementacions i resultats experimentals. Aquests resultats s'utilitzen més endevant en aquesta tesi per a l'anàlisi de problemes d'unificació i matching amb variables de contexte i entrada comprimida. A la resta d'aquesta tesi ens centrem en variants d'unificació amb variables de contexte, que són un cas particular de variables d'ordre superior. Més concretament, a la segona part d'aquesta tesi, ens centrem en un cas particular d'unificació de contextes on nomès es permet una sola variable de context en l'entrada. Aquest problema s'anomena "one context unification". Mostrem que aquest problema es pot resoldre en temps polinòmic indeterminista, no només quan els termes de l'entrada són representats de forma explícita, sino també quan es representen fent servir grafs dirigits acíclics i gramàtiques singletó. També presentem un resultat parcial recent en la tasca de trobar un algorisme de temps polinòmic per a one context unification. Al final de la tesi, estudiem el problema de matching per a entrades amb variables de contexte, que és un cas particular d'unificació on només un dels dos termes de cada equació té variables. En contrast amb el problema general, matching de contextes ha estat classificat com a problema NP-complet. Mostrem que matching de contextes és NP-complet fins i tot quan es fan servir gramàtiques com a formalisme de representació de termes. Això implica que afegir compressió no ens porta a un augment dràstic de la complexitat del problema. També demostrem que la restricció de matching de contextes on el nombre de variables de contexte està afitat per una constant fixa del problema es pot resoldre en temps polinòmic, fins i tot quan es fan servir grafs dirigits acíclics com formalisme de representació de terme

TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service

Machine learning methods are widely used for a variety of prediction problems. \emph{Prediction as a service} is a paradigm in which service providers with technological expertise and computational resources may perform predictions for clients. However, data privacy severely restricts the applicability of such services, unless measures to keep client data private (even from the service provider) are designed. Equally important is to minimize the amount of computation and communication required between client and server. Fully homomorphic encryption offers a possible way out, whereby clients may encrypt their data, and on which the server may perform arithmetic computations. The main drawback of using fully homomorphic encryption is the amount of time required to evaluate large machine learning models on encrypted data. We combine ideas from the machine learning literature, particularly work on binarization and sparsification of neural networks, together with algorithmic tools to speed-up and parallelize computation using encrypted data.Comment: Accepted at International Conference in Machine Learning (ICML), 201

Data Generation for Neural Programming by Example

Programming by example is the problem of synthesizing a program from a small set of input / output pairs. Recent works applying machine learning methods to this task show promise, but are typically reliant on generating synthetic examples for training. A particular challenge lies in generating meaningful sets of inputs and outputs, which well-characterize a given program and accurately demonstrate its behavior. Where examples used for testing are generated by the same method as training data then the performance of a model may be partly reliant on this similarity. In this paper we introduce a novel approach using an SMT solver to synthesize inputs which cover a diverse set of behaviors for a given program. We carry out a case study comparing this method to existing synthetic data generation procedures in the literature, and find that data generated using our approach improves both the discriminatory power of example sets and the ability of trained machine learning models to generalize to unfamiliar data

Distributed Vector-OLE: Improved Constructions and Implementation

We investigate concretely efficient protocols for distributed oblivious linear evaluation over vectors (Vector-OLE). Boyle et al. (CCS 2018) proposed a protocol for secure distributed pseudorandom Vector-OLE generation using sublinear communication, but they did not provide an implementation. Their construction is based on a variant of the LPN assumption and assumes a distributed key generation protocol for single-point Function Secret Sharing (FSS), as well as an efficient batching scheme to obtain multi-point FSS. We show that this requirement can be relaxed, resulting in a weaker variant of FSS, for which we give an efficient protocol. This allows us to use efficient probabilistic batch codes that were also recently used for batched PIR by Angel et al. (S&P 2018). We construct a full Vector-OLE generator from our protocols, and compare it experimentally with alternative approaches. Our implementation parallelizes very well, and has low communication overhead in practice. For generating a VOLE of size $2^{20}$, our implementation only takes $0.52$s on 32 cores

Secure and Scalable Document Similarity on Distributed Databases: Differential Privacy to the Rescue

Privacy-preserving collaborative data analysis enables richer models than what each party can learn with their own data. Secure Multi-Party Computation (MPC) offers a robust cryptographic approach to this problem, and in fact several protocols have been proposed for various data analysis and machine learning tasks. In this work, we focus on secure similarity computation between text documents, and the application to $k$-nearest neighbors (\knn) classification. Due to its non-parametric nature, \knn presents scalability challenges in the MPC setting. Previous work addresses these by introducing non-standard assumptions about the abilities of an attacker, for example by relying on non-colluding servers. In this work, we tackle the scalability challenge from a different angle, and instead introduce a secure preprocessing phase that reveals differentially private (DP) statistics about the data. This allows us to exploit the inherent sparsity of text data and significantly speed up all subsequent classifications

Blind Justice: Fairness with Encrypted Sensitive Attributes

Recent work has explored how to train machine learning models which do not discriminate against any subgroup of the population as determined by sensitive attributes such as gender or race. To avoid disparate treatment, sensitive attributes should not be considered. On the other hand, in order to avoid disparate impact, sensitive attributes must be examined, e.g., in order to learn a fair model, or to check if a given model is fair. We introduce methods from secure multi-party computation which allow us to avoid both. By encrypting sensitive attributes, we show how an outcome-based fair model may be learned, checked, or have its outputs verified and held to account, without users revealing their sensitive attributes.Comment: published at ICML 201