Results 1 - 10
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52
Probabilistically accurate program transformations
- In SAS
, 2011
"... Abstract. The standard approach to program transformation involves the use of discrete logical reasoning to prove that the transformation does not change the observable semantics of the program. We propose a new approach that, in contrast, uses probabilistic reasoning to justify the application of t ..."
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Cited by 38 (14 self)
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Abstract. The standard approach to program transformation involves the use of discrete logical reasoning to prove that the transformation does not change the observable semantics of the program. We propose a new approach that, in contrast, uses probabilistic reasoning to justify the application of transformations that may change, within probabilistic accuracy bounds, the result that the program produces. Our new approach produces probabilistic guarantees of the form P(|D | ≥ B) ≤ ɛ, ɛ ∈ (0, 1), where D is the difference between the results that the transformed and original programs produce, B is an acceptability bound on the absolute value of D, and ɛ is the maximum acceptable probability of observing large |D|. We show how to use our approach to justify the application of loop perforation (which transforms loops to execute fewer iterations) to a set of computational patterns. 1
Proving Programs Robust ∗
"... We present a program analysis for verifying quantitative robustness properties of programs, stated generally as: “If the inputs of a program are perturbed by an arbitrary amount ɛ, then its outputs change at most by Kɛ, where K can depend on the size of the input but not its value. ” Robustness prop ..."
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Cited by 36 (6 self)
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We present a program analysis for verifying quantitative robustness properties of programs, stated generally as: “If the inputs of a program are perturbed by an arbitrary amount ɛ, then its outputs change at most by Kɛ, where K can depend on the size of the input but not its value. ” Robustness properties generalize the analytic notion of continuity—e.g., while the function e x is continuous, it is not robust. Our problem is to verify the robustness of a function P that is coded as an imperative program, and can use diverse data types and features such as branches and loops. Our approach to the problem soundly decomposes it into two subproblems: (a) verifying that the smallest possible perturbations to the inputs of P do not change the corresponding outputs significantly, even if control now flows
Verifying Quantitative Reliability for Programs That Execute on Unreliable Hardware
"... Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approx ..."
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Cited by 25 (3 self)
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Emerging high-performance architectures are anticipated to contain unreliable components that may exhibit soft errors, which silently corrupt the results of computations. Full detection and masking of soft errors is challenging, expensive, and, for some applications, unnecessary. For example, approximate computing applications (such as multimedia processing, machine learning, and big data analytics) can often naturally tolerate soft errors. We present Rely, a programming language that enables developers to reason about the quantitative reliability of an application – namely, the probability that it produces the correct result when executed on unreliable hardware. Rely allows developers to specify the reliability requirements for each value that a function produces. We present a static quantitative reliability analysis that verifies quantitative requirements on the reliability of an application, enabling a developer to perform sound and verified reliability engineering. The analysis takes a Rely program with a reliability specification and a hardware specification that characterizes the reliability of the underlying hardware components and verifies that the program satisfies its reliability specification when executed on the underlying unreliable hardware platform. We demonstrate the application of quantitative reliability analysis on six computations implemented
Geo-indistinguishability: Differential privacy for location-based systems
, 2012
"... The growing popularity of location-based systems, allowing un-known/untrusted servers to easily collect huge amounts of informa-tion regarding users ’ location, has recently started raising serious privacy concerns. In this paper we introduce geo-indistinguisha-bility, a formal notion of privacy for ..."
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Cited by 24 (5 self)
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The growing popularity of location-based systems, allowing un-known/untrusted servers to easily collect huge amounts of informa-tion regarding users ’ location, has recently started raising serious privacy concerns. In this paper we introduce geo-indistinguisha-bility, a formal notion of privacy for location-based systems that protects the user’s exact location, while allowing approximate in-formation – typically needed to obtain a certain desired service – to be released. This privacy definition formalizes the intuitive notion of protect-ing the user’s location within a radius r with a level of privacy that depends on r, and corresponds to a generalized version of the well-known concept of differential privacy. Furthermore, we present a mechanism for achieving geo-indistinguishability by adding con-trolled random noise to the user’s location. We describe how to use our mechanism to enhance LBS appli-cations with geo-indistinguishability guarantees without compro-mising the quality of the application results. Finally, we compare state-of-the-art mechanisms from the literature with ours. It turns out that, among all mechanisms independent of the prior, our mech-anism offers the best privacy guarantees.
Relational verification using product programs
- In Formal Methods, Lecture Notes in Computer Science
, 2011
"... Abstract. Relational program logics are formalisms for specifying and verifying properties about two programs or two runs of the same pro-gram. These properties range from correctness of compiler optimizations or equivalence between two implementations of an abstract data type, to properties like no ..."
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Cited by 21 (4 self)
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Abstract. Relational program logics are formalisms for specifying and verifying properties about two programs or two runs of the same pro-gram. These properties range from correctness of compiler optimizations or equivalence between two implementations of an abstract data type, to properties like non-interference or determinism. Yet the current tech-nology for relational verification remains underdeveloped. We provide a general notion of product program that supports a direct reduction of re-lational verification to standard verification. We illustrate the benefits of our method with selected examples, including non-interference, standard loop optimizations, and a state-of-the-art optimization for incremental computation. All examples have been verified using the Why tool. 1
Differential Privacy Under Fire
"... Anonymizing private data before release is not enough to reliably protect privacy, as Netflix and AOL have learned to their cost. Recent research on differential privacy opens a way to obtain robust, provable privacy guarantees, and systems like PINQ and Airavat now offer convenient frameworks for p ..."
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Cited by 21 (4 self)
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Anonymizing private data before release is not enough to reliably protect privacy, as Netflix and AOL have learned to their cost. Recent research on differential privacy opens a way to obtain robust, provable privacy guarantees, and systems like PINQ and Airavat now offer convenient frameworks for processing arbitrary userspecified queries in a differentially private way. However, these systems are vulnerable to a variety of covertchannel attacks that can be exploited by an adversarial querier. We describe several different kinds of attacks, all feasible in PINQ and some in Airavat. We discuss the space of possible countermeasures, and we present a detailed design for one specific solution, based on a new primitive we call predictable transactions and a simple differentially private programming language. Our evaluation, which relies on a proof-of-concept implementation based on the Caml Light runtime, shows that our design is effective against remotely exploitable covert channels, at the expense of a higher query completion time. 1
Linear Dependent Types for Differential Privacy
"... Differential privacy offers a way to answer queries about sensitive information while providing strong, provable privacy guarantees, ensuring that the presence or absence of a single individual in the database has a negligible statistical effect on the query’s result. Proving that a given query has ..."
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Cited by 16 (7 self)
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Differential privacy offers a way to answer queries about sensitive information while providing strong, provable privacy guarantees, ensuring that the presence or absence of a single individual in the database has a negligible statistical effect on the query’s result. Proving that a given query has this property involves establishing a bound on the query’s sensitivity—how much its result can change when a single record is added or removed. A variety of tools have been developed for certifying that a given query is differentially private. In one approach, Reed and Pierce [34] proposed a functional programming language, Fuzz, for writing differentially private queries. Fuzz uses linear types to track sensitivity and a probability monad to express randomized computation; it guarantees that any program with a certain type is differentially private. Fuzz can successfully verify many useful queries. However, it fails when the sensitivity analysis depends on values that are not known statically. We present DFuzz, an extension of Fuzz with a combination of linear indexed types and lightweight dependent types. This combination allows a richer sensitivity analysis that is able to certify a larger class of queries as differentially private, including ones whose sensitivity depends on runtime information. As in Fuzz, the differential privacy guarantee follows directly from the soundness theorem of the type system. We demonstrate the enhanced expressivity of DFuzz by certifying differential privacy for a broad class of iterative algorithms that could not be typed previously. Categories and Subject Descriptors D.3.2 [Programming Languages]: Language Classifications—Specialized application languages;
doi:10.1145/2240236.2240262 Continuity and Robustness of Programs
"... Computer scientists have long believed that software is different from physical systems in one fundamental way: while the latter have continuous dynamics, the former do not. In this paper, we argue that notions of continuity from mathematical analysis are relevant and interesting even for software. ..."
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Cited by 15 (4 self)
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Computer scientists have long believed that software is different from physical systems in one fundamental way: while the latter have continuous dynamics, the former do not. In this paper, we argue that notions of continuity from mathematical analysis are relevant and interesting even for software. First, we demonstrate that many everyday programs are continuous (i.e., arbitrarily small changes to their inputs only cause arbitrarily small changes to their outputs) or Lipschitz continuous (i.e., when their inputs change, their outputs change at most proportionally). Second, we give an mostly-automatic framework for verifying that a program is continuous or Lipschitz, showing that traditional, discrete approaches to proving programs correct can be extended to reason about these properties. An immediate application of our analysis is in reasoning about the robustness of programs that execute on uncertain inputs. In the longer run, it raises hopes for a toolkit for reasoning about programs that freely combines logical and analytical mathematics. 1.
Measure Transformer Semantics for Bayesian Machine Learning
"... Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expres ..."
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Cited by 15 (3 self)
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Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (that is, a prior distribution) and a set of observations of variables. There is a trend in machine learning towards expressing Bayesian models as probabilistic programs. As a foundation for this kind of programming, we propose a core functional calculus with primitives for sampling prior distributions and observing variables. We define combinators for measure transformers, based on theorems in measure theory, and use these to give a rigorous semantics to our core calculus. The original features of our semantics include its support for discrete, continuous, and hybrid measures, and, in particular, for observations of zero-probability events. We compile our core language to a small imperative language that has a straightforward semantics via factor graphs, data structures that enable many efficient inference algorithms. We use an existing inference engine for efficient approximate inference of posterior marginal distributions, treating thousands of observations per second for large instances of realistic models. 1
The Effects of
- Artificial Sources of Water on Rangeland Biodiversity. Environment Australia and CSIRO
, 1997
"... “Turing hoped that his abstracted-paper-tape model was so simple, so transparent and well defined, that it would not depend on any assumptions about physics that could conceivably be falsified, and therefore that it could become the basis of an abstract theory of computation that was independent of ..."
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Cited by 14 (5 self)
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“Turing hoped that his abstracted-paper-tape model was so simple, so transparent and well defined, that it would not depend on any assumptions about physics that could conceivably be falsified, and therefore that it could become the basis of an abstract theory of computation that was independent of the underlying physics. ‘He thought, ’ as Feynman once put it, ‘that he understood paper. ’ But he was mistaken. Real, quantum-mechanical paper is wildly different from the abstract stuff that the Turing machine uses. The Turing machine is entirely classical...”
