We present Argos, a simple approach for adding verifiability to fully homomorphic encryption (FHE) schemes using trusted hardware. Traditional approaches to verifiable FHE require expensive cryptographic proofs, which incur an overhead of up to seven orders of magnitude on top of FHE, making them impractical. With Argos, we show that trusted hardware can be securely used to provide verifiability for FHE computations, with minimal overhead relative to the baseline FHE computation. An important contribution of Argos is showing that the major security pitfall associated with trusted hardware, microarchitectural side channels, can be completely mitigated by excluding any secrets from the CPU and the memory hierarchy. This is made possible by focusing on building a platform that only enforces program and data integrity and not confidentiality (which is sufficient for verifiable FHE, since all data remain encrypted at all times). All secrets related to the attestation mechanism are kept in a separate coprocessor (e.g., a TPM)---inaccessible to any software-based attacker. Relying on a discrete TPM typically incurs significant performance overhead, which is why (insecure) software-based TPMs are used in practice. As a second contribution, we show that for FHE applications, the attestation protocol can be adapted to only incur a fixed cost. Argos requires no dedicated hardware extensions and is supported on commodity processors from 2008 onward. Our prototype implementation introduces 3% overhead for FHE evaluation, and 8% for more complex protocols. In particular, we show that Argos can be used for real-world applications of FHE, such as private information retrieval (PIR) and private set intersection (PSI), where providing verifiability is imperative. By demonstrating how to combine cryptography with trusted hardware, Argos paves the way for widespread deployment of FHE-based protocols beyond the semi-honest setting, without the overhead of cryptographic proofs.

SUMMARY: Large volumes of data generated by high-throughput sequencing instruments present non-trivial challenges in data storage, content access and transfer. We present G-SQZ, a Huffman coding-based sequencing-reads-specific representation scheme that compresses data without altering the relative order. G-SQZ has achieved from 65% to 81% compression on benchmark datasets, and it allows selective access without scanning and decoding from start. This article focuses on describing the underlying encoding scheme and its software implementation, and a more theoretical problem of optimal compression is out of scope. The immediate practical benefits include reduced infrastructure and informatics costs in managing and analyzing large sequencing data. AVAILABILITY: http://public.tgen.org/sqz. Academic/non-profit: SOURCE: available at no cost under a non-open-source license by requesting from the web-site; Binary: available for direct download at no cost. For-Profit: Submit request for for-profit license from the web-site.

MOTIVATION: Given a large set of potential features, such as the set of all gene-expression values from a microarray, it is necessary to find a small subset with which to classify. The task of finding an optimal feature set of a given size is inherently combinatoric because to assure optimality all feature sets of a given size must be checked. Thus, numerous suboptimal feature-selection algorithms have been proposed. There are strong impediments to evaluate feature-selection algorithms using real data when data are limited, a common situation in genetic classification. The difficulty is compound. First, there are no class-conditional distributions from which to draw data points, only a single small labeled sample. Second, there are no test data with which to estimate the feature-set errors, and one must depend on a training-data-based error estimator. Finally, there is no optimal feature set with which to compare the feature sets found by the algorithms. RESULTS: This paper describes a genetic test bed for the evaluation of feature-selection algorithms. It begins with a large biological feature-label dataset that is used as an empirical distribution and, using massively parallel computation, finds the top feature sets of various sizes based on a given sample size and classification rule. The user can draw random samples from the data, apply a proposed algorithm, and evaluate the proficiency of the proposed algorithm via three different measures (code provided). A key feature of the test bed is that, once a dataset is input, a single command creates the entire test bed relative to the dataset. The particular dataset used for the first version of the test bed comes from a microarray-based classification study that analyzes a large number of microarrays, prepared with RNA from breast tumor samples from each of 295 patients. AVAILABILITY: The software and supplementary material are available at http://public.tgen.org/tgen-cb/support/testbed/ CONTACT: [email protected].

MOTIVATION: Given the joint feature-label distribution, increasing the number of features always results in decreased classification error; however, this is not the case when a classifier is designed via a classification rule from sample data. Typically (but not always), for fixed sample size, the error of a designed classifier decreases and then increases as the number of features grows. The potential downside of using too many features is most critical for small samples, which are commonplace for gene-expression-based classifiers for phenotype discrimination. For fixed sample size and feature-label distribution, the issue is to find an optimal number of features. RESULTS: Since only in rare cases is there a known distribution of the error as a function of the number of features and sample size, this study employs simulation for various feature-label distributions and classification rules, and across a wide range of sample and feature-set sizes. To achieve the desired end, finding the optimal number of features as a function of sample size, it employs massively parallel computation. Seven classifiers are treated: 3-nearest-neighbor, Gaussian kernel, linear support vector machine, polynomial support vector machine, perceptron, regular histogram and linear discriminant analysis. Three Gaussian-based models are considered: linear, nonlinear and bimodal. In addition, real patient data from a large breast-cancer study is considered. To mitigate the combinatorial search for finding optimal feature sets, and to model the situation in which subsets of genes are co-regulated and correlation is internal to these subsets, we assume that the covariance matrix of the features is blocked, with each block corresponding to a group of correlated features. Altogether there are a large number of error surfaces for the many cases. These are provided in full on a companion website, which is meant to serve as resource for those working with small-sample classification. AVAILABILITY: For the companion website, please visit http://public.tgen.org/tamu/ofs/ CONTACT: [email protected].