Secure Multi-Party Computation In Practice

Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC provides strong privacy guarantees, but practical adoption requires high-quality application design, software development, and resource management. This dissertation aims to identify and reduce barriers to practical deployment of MPC applications. First, the dissertation evaluates the design, capabilities, and usability of eleven state-of-the-art MPC software frameworks. These frameworks are essential for prototyping MPC applications, but their qualities vary widely; the survey provides insight into their current abilities and limitations. A comprehensive online repository augments the survey, including complete build environments, sample programs, and additional documentation for each framework. Second, the dissertation applies these lessons in two practical applications of MPC. The first addresses algorithms for assessing stability in financial networks, traditionally designed in a full-information model with a central regulator or data aggregator. This case study describes principles to transform two such algorithms into data-oblivious versions and benchmark their execution under MPC using three frameworks. The second aims to enable unlinkability of payments made with blockchain-based cryptocurrencies. This study uses MPC in conjunction with other privacy techniques to achieve unlinkability in payment channels. Together, these studies illuminate the limitations of existing software, develop guidelines for transforming non-private algorithms into versions suitable for execution under MPC, and illustrate the current practical feasibility of MPC as a solution to a wide variety of applications

The Proof is in the Pudding: Proofs of Work for Solving Discrete Logarithms

We propose a proof of work protocol that computes the discrete logarithm of an element in a cyclic group. Individual provers generating proofs of work perform a distributed version of the Pollard rho algorithm. Such a protocol could capture the computational power expended to construct proof-of-work-based blockchains for a more useful purpose, as well as incentivize advances in hardware, software, or algorithms for an important cryptographic problem. We describe our proposed construction and elaborate on challenges and potential trade-offs that arise in designing a practical proof of work

Privacy-preserving network analytics

We develop a new privacy-preserving framework for a general class of financial network models, leveraging cryptographic principles from secure multiparty computation and decentralized systems. We show how aggregate-level network statistics required for stability assessment and stress testing can be derived from real data without any individual node revealing its private information to any outside party, be it other nodes in the network, or even a central agent. Our work bridges the gap between established theories of financial network contagion and systemic risk that assume agents have full network information and the real world where information sharing is hindered by privacy and security concerns. This paper was accepted by Agostino Capponi, finance. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2022.4582 .https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3680000Othe

Measuring small subgroup attacks against Diffie-Hellman

Several recent standards, including NIST SP 800- 56A and RFC 5114, advocate the use of “DSA” parameters for Diffie-Hellman key exchange. While it is possible to use such parameters securely, additional validation checks are necessary to prevent well-known and potentially devastating attacks. In this paper, we observe that many Diffie-Hellman implementations do not properly validate key exchange inputs. Combined with other protocol properties and implementation choices, this can radically decrease security. We measure the prevalence of these parameter choices in the wild for HTTPS, POP3S, SMTP with STARTTLS, SSH, IKEv1, and IKEv2, finding millions of hosts using DSA and other non-“safe” primes for Diffie-Hellman key exchange, many of them in combination with potentially vulnerable behaviors. We examine over 20 open-source cryptographic libraries and applications and observe that until January 2016, not a single one validated subgroup orders by default. We found feasible full or partial key recovery vulnerabilities in OpenSSL, the Exim mail server, the Unbound DNS client, and Amazon’s load balancer, as well as susceptibility to weaker attacks in many other applications

Diketo acids inhibit the cap-snatching endonuclease of several Bunyavirales

Several fatal bunyavirus infections lack specific treatment. Here, we show that diketo acids engage a panel of bunyavirus cap-snatching endonucleases, inhibit their catalytic activity and reduce viral replication of a taxonomic representative in vitro. Specifically, the non-salt form of L-742,001 and its derivatives exhibited EC50 values between 5.6 to 6.9 μM against a recombinant BUNV-mCherry virus. Structural analysis and molecular docking simulations identified traits of both the class of chemical entities and the viral target that could help the design of novel, more potent molecules for the development of pan-bunyavirus antivirals

Neuroserpin polymorphisms and stroke risk in a biracial population: the stroke prevention in young women study

<p>Abstract</p> <p>Background</p> <p>Neuroserpin, primarily localized to CNS neurons, inhibits the adverse effects of tissue-type plasminogen activator (tPA) on the neurovascular unit and has neuroprotective effects in animal models of ischemic stroke. We sought to evaluate the association of neuroserpin polymorphisms with risk for ischemic stroke among young women.</p> <p>Methods</p> <p>A population-based case-control study of stroke among women aged 15–49 identified 224 cases of first ischemic stroke (47.3% African-American) and 211 age-matched control subjects (43.1% African-American). Neuroserpin single nucleotide polymorphisms (SNPs) chosen through HapMap were genotyped in the study population and assessed for association with stroke.</p> <p>Results</p> <p>Of the five SNPs analyzed, the A allele (frequency; Caucasian = 0.56, African-American = 0.42) of SNP rs6797312 located in intron 1 was associated with stroke in an age-adjusted dominant model (AA and AT vs. TT) among Caucasians (OR = 2.05, p = 0.023) but not African-Americans (OR = 0.71, p = 0.387). Models adjusting for other risk factors strengthened the association. Race-specific haplotype analyses, inclusive of SNP rs6797312, again demonstrated significant associations with stroke among Caucasians only.</p> <p>Conclusion</p> <p>This study provides the first evidence that neuroserpin is associated with early-onset ischemic stroke among Caucasian women.</p

Secure Multi-Party Computation in Practice

Secure multi-party computation (MPC) is a cryptographic primitive for computing on private data. MPC provides strong privacy guarantees, but practical adoption requires high-quality application design, software development, and resource management. This dissertation aims to identify and reduce barriers to practical deployment of MPC applications. First, the dissertation evaluates the design, capabilities, and usability of eleven state-of-the-art MPC software frameworks. These frameworks are essential for prototyping MPC applications, but their qualities vary widely; the survey provides insight into their current abilities and limitations. A comprehensive online repository augments the survey, including complete build environments, sample programs, and additional documentation for each framework. Second, the dissertation applies these lessons in two practical applications of MPC. The first addresses algorithms for assessing stability in financial networks, traditionally designed in a full-information model with a central regulator or data aggregator. This case study describes principles to transform two such algorithms into data-oblivious versions and benchmark their execution under MPC using three frameworks. The second aims to enable unlinkability of payments made with blockchain-based cryptocurrencies. This study uses MPC in conjunction with other privacy techniques to achieve unlinkability in payment channels. Together, these studies illuminate the limitations of existing software, develop guidelines for transforming non-private algorithms into versions suitable for execution under MPC, and illustrate the current practical feasibility of MPC as a solution to a wide variety of applications

EVA: An Encrypted Vector Arithmetic Language and Compiler for Efficient Homomorphic Computation

Fully-Homomorphic Encryption (FHE) offers powerful capabilities by enabling secure offloading of both storage and computation, and recent innovations in schemes and implementations have made it all the more attractive. At the same time, FHE is notoriously hard to use with a very constrained programming model, a very unusual performance profile, and many cryptographic constraints. Existing compilers for FHE either target simpler but less efficient FHE schemes or only support specific domains where they can rely on expert-provided high-level runtimes to hide complications.This paper presents a new FHE language called Encrypted Vector Arithmetic (EVA), which includes an optimizing compiler that generates correct and secure FHE programs, while hiding all the complexities of the target FHE scheme. Bolstered by our optimizing compiler, programmers can develop efficient general-purpose FHE applications directly in EVA. For example, we have developed image processing applications using EVA, with a very few lines of code.EVA is designed to also work as an intermediate representation that can be a target for compiling higher-level domain-specific languages. To demonstrate this, we have re-targeted CHET, an existing domain-specific compiler for neural network inference, onto EVA. Due to the novel optimizations in EVA, its programs are on average 5.3x faster than those generated by CHET. We believe that EVA would enable a wider adoption of FHE by making it easier to develop FHE applications and domain-specific FHE compilers