Search for: All records

Memory-trace Obliviousness (MTO) is a noninterference property: programs that enjoy it have neither explicit nor implicit information leaks, even when the adversary can observe the program counter and the address trace of memory accesses. Probabilistic MTO relaxes MTO to accept probabilistic programs. In prior work, we developed λobliv, whose type system aims to enforce PMTO [2]. We showed that lambda-obliv could typecheck (recursive) Tree ORAM [6], a sophisticated algorithm that implements a probabilistically oblivious key-value store. We conjectured that λobliv ought to be able to typecheck more optimized oblivious data structures (ODSs)[8], but that its type system was as yet too weak.In this short paper we show we were wrong: ODSs cannot be implemented in lambda-obliv because they are not actually PMTO, due to the possibility of overflow, which occurs when a oram_write silently fails due to a local lack of space. This was surprising to us because Tree ORAM can also overflow but is still PMTO. The paper explains what is going on and sketches the task of adapting the PMTO property, and lambda-obliv's type system, to characterize ODS security. 

Faceted execution is a linguistic paradigm for dynamic information-flow control with the distinguishing feature that program values may be faceted. Such values represent multiple versions or facets at once, for different security labels. This enables policy-agnostic programming: a paradigm permitting expressive privacy policies to be declared, independent of program logic. Although faceted execution prevents information leakage at runtime, it does not guarantee the absence of failure due to policy violations. By contrast with static mechanisms (such as security type systems), dynamic information-flow control permits arbitrarily expressive and dynamic privacy policies but imposes significant runtime overhead and delays discovery of any possible violations. In this paper, we present the two different abstract interpretations for faceted execution in the presence of first-class policies. We first present an abstraction which allows one to reason statically about the shape of facets at each program point. This abstraction is useful for statically proving the absence of runtime errors and eliminating runtime checks related to facets. Reasoning statically about the contents of faceted values, however, is complicated by the presence of first-class security labels, especially because abstract labels may conflate more than one runtime label. To address these issues, we also develop a more precise abstraction that relies on an analysis tracking singleton heap abstractions. We present an implementation of our coarse abstraction in Racket and demonstrate its performance on several sample programs. We conclude by showing how our precise domain can be used to verify information-flow properties. 

An oblivious computation is one that is free of direct and indirect information leaks, e.g., due to observable differences in timing and memory access patterns. This paper presents Lambda Obliv, a core language whose type system enforces obliviousness. Prior work on type-enforced oblivious computation has focused on deterministic programs. Lambda Obliv is new in its consideration of programs that implement probabilistic algorithms, such as those involved in cryptography. Lambda Obliv employs a substructural type system and a novel notion of probability region to ensure that information is not leaked via the observed distribution of visible events. Probability regions support reasoning about probabilistic correlation and independence between values, and our use of probability regions is motivated by a source of unsoundness that we discovered in the type system of ObliVM, a language for implementing state of the art oblivious algorithms. We prove that Lambda Obliv's type system enforces obliviousness and show that it is expressive enough to typecheck advanced tree-based oblivious RAMs. 

Differential privacy offers a formal privacy guarantee for individuals, but many deployments of differentially private systems require a trusted third party (the data curator). We propose DuetSGX, a system that uses secure hardware (Intel’s SGX) to eliminate the need for a trusted data curator. Data owners submit encrypted data that can be decrypted only within a secure enclave running the DuetSGX system, ensuring that sensitive data is never available to the data curator. Analysts submit queries written in the Duet language, which is specifically designed for verifying that programs satisfy differential privacy; DuetSGX uses the Duet typechecker to verify that each query satisfies differential privacy before running it. DuetSGX therefore provides the benefits of local differential privacy and central differential privacy simultaneously: noise is only added to final results, and there is no trusted third party. We have implemented a proof-of-concept implementation of DuetSGX and we release it as open-source. 

Computer networks often serve as the first line of defense against malicious attacks. Although there are a growing number of software defined networking (SDN) tools for defining and enforcing security policies, most assume a single administrative domain and are unable to handle the challenges that arise in networks that could beneficially be programmed by multiple administrative domains. For example, consumers may want want to allow their home IoT networks to be configured by device vendors, which raises security and privacy concerns. In this paper we propose a framework called Proof Carrying Network Code (PCNC) for specifying and enforcing security in SDNs with interacting administrative domains. Like Proof Carrying Authorization (PCA), PCNC provides methods for authorization domains for network reprogramming, and like Proof Carrying Code (PCC), PCNC provides methods for enforcing desired behavior of network programs. We develop theoretical foundations for PCNC and evaluate it in simulated and real network settings, in a case study that considers security in IoT networks for at-home health monitoring. 

During the past decade, differential privacy has become the gold standard for protecting the privacy of individuals. However, verifying that a particular program provides differential privacy often remains a manual task to be completed by an expert in the field. Language-based techniques have been proposed for fully automating proofs of differential privacy via type system design, however these results have lagged behind advances in differentially-private algorithms, leaving a noticeable gap in programs which can be automatically verified while also providing state-of-the-art bounds on privacy. We propose Duet, an expressive higher-order language, linear type system and tool for automatically verifying differential privacy of general-purpose higher-order programs. In addition to general purpose programming, Duet supports encoding machine learning algorithms such as stochastic gradient descent, as well as common auxiliary data analysis tasks such as clipping, normalization and hyperparameter tuning - each of which are particularly challenging to encode in a statically verified differential privacy framework. We present a core design of the Duet language and linear type system, and complete key proofs about privacy for well-typed programs. We then show how to extend Duet to support realistic machine learning applications and recent variants of differential privacy which result in improved accuracy for many practical differentially private algorithms. Finally, we implement several differentially private machine learning algorithms in Duet which have never before been automatically verified by a language-based tool, and we present experimental results which demonstrate the benefits of Duet's language design in terms of accuracy of trained machine learning models.