Next-generation sequencing technologies have significantly increased the availability of genomic databases. These databases can be used for numerous applications in genome-wide association studies, such as disease prediction and precision medicine. Missing values in genotype data diminish the database quality. Genotype imputation is an essential low-cost tool in genomics that statistically infers missing genotype variants from an experimentally observed set of variants. Owing to the computationally intensive nature of the problem, genotype imputation is often outsourced to an external service provider. However, sharing genomic data as a plaintext raises privacy concerns and leaks sensitive information. Existing privacy-preserving approaches perform genotype imputation using linear classification models, which require training a separate classifier for each variant using previously labeled data. Self-supervised deep learning models, such as autoencoders, have recently become popular because of their ability to model complex patterns in genomic datasets and achieve significantly high accuracy. However, deep learning-based genotype imputation under a privacy-preserving setting remains largely unexplored. In this work, we propose a novel adaptation of an autoencoder-based genotype imputation model that preserves the privacy of sensitive genomic data. To our knowledge, ours is the first work to do so. Genomic data privacy is preserved using fully homomorphic encryption (FHE). FHE schemes enable Efficient computations over encrypted data but suffer from noise growth as the number of computations increases. To overcome the issue of noise growth due to computationally complex deep learning, we use neural network quantization, which considerably reduces the network size while achieving high accuracy, as demonstrated in our results. We present all the necessary parameters to perform deep learning-based genotype imputation in the privacy-preserving setting.

Patient-centric precision medicine requires the analysis of large volumes of genomic data to tailor treatments and medications based on individual-level characteristics. Because the amount of data held by a single institution is limited, researchers may want access to genomic data held by other institutions. Owing to the inherent privacy implications of genomic data, performing comparisons on encrypted data is preferable in certain settings. The Similar patient query (SPQ) is an application that enables a secure search across genomic databases for patients with similar genetic makeup. Query results can be used to draw meaningful conclusions regarding suitable therapies. However, existing protocols either reveal intermediate computations, such as similarity scores, which can lead to membership-inference attacks, or they realize the ideal Boolean output (similar/not similar) through multiple protocol rounds, requiring the database owners to stay online throughout. This paper introduces a two-party privacy-preserving approach to perform SPQs across encrypted genomic databases based on secure function extensions of additively homomorphic encryption. In contrast to related works, our scheme enables secure computation of genomic data similarity without an external party in a single round. This is achieved for more than 1000 positions of a genome in a single public key operation of 256-bit security level in the integer factorization setting.

Real-world applications of record linkage often require matching to be robust in spite of small variations in string fields. For example, two health care providers should be able to detect a patient in common, even if one record contains a typo or transcription error. In the privacy-preserving setting, however, the problem of approximate string matching has been cast as a trade-off between security and practicality, and the literature has mainly focused on Bloom filter encodings, an approach which can leak significant information about the underlying records. We present a novel public-key construction for secure two-party evaluation of threshold functions in restricted domains based on embeddings found in the message spaces of additively homomorphic encryption schemes. We use this to construct an efficient two-party protocol for privately computing the threshold Dice coefficient. Relative to the approach of Bloom filter encodings, our proposal offers formal security guarantees and greater matching accuracy. We implement the protocol and demonstrate the feasibility of this approach in linking medium-sized patient databases with tens of thousands of records.

Network attacks have been very prevalent as their rate is growing tremendously. Both organization and individuals are now concerned about their confidentiality, integrity and availability of their critical information which are often impacted by network attacks. To that end, several previous machine learning-based intrusion detection methods have been developed to secure network infrastructure from such attacks. In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique to tune the parameters of Support Vector Machine with Gaussian Kernel (SVM-RBF), Random Forest (RF), and k-Nearest Neighbor (k-NN) algorithms. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.

OBJECTIVE: Some phase 1 clinical trials offer strong financial incentives for healthy individuals to participate in their studies. There is evidence that some individuals enroll in multiple trials concurrently. This creates safety risks and introduces data quality problems into the trials. Our objective was to construct a privacy preserving protocol to track phase 1 participants to detect concurrent enrollment. DESIGN: A protocol using secure probabilistic querying against a database of trial participants that allows for screening during telephone interviews and on-site enrollment was developed. The match variables consisted of demographic information. MEASUREMENT: The accuracy (sensitivity, precision, and negative predictive value) of the matching and its computational performance in seconds were measured under simulated environments. Accuracy was also compared to non-secure matching methods. RESULTS: The protocol performance scales linearly with the database size. At the largest database size of 20,000 participants, a query takes under 20s on a 64 cores machine. Sensitivity, precision, and negative predictive value of the queries were consistently at or above 0.9, and were very similar to non-secure versions of the protocol. CONCLUSION: The protocol provides a reasonable solution to the concurrent enrollment problems in phase 1 clinical trials, and is able to ensure that personal information about participants is kept secure.

In the above titled paper (ibid., vol. 4, no. 4, pp. 611-627, Dec. 09), due to a production error, the affiliations of two of the authors were listed incorrectly. The correct affiliations are presented here. Also, the name of the last author in the affiliations footnote was printed incorrectly. The correct name is P. Y. A. Ryan.

Scantegrity II is an enhancement for existing paper ballot systems. It allows voters to verify election integrity - from their selections on the ballot all the way to the final tally - by noting codes and checking for them online. Voters mark Scantegrity II ballots just as with conventional optical scan, but using a special ballot marking pen. Marking a selection with this pen makes legible an otherwise invisible preprinted confirmation code. Confirmation codes are independent and random for each potential selection on each ballot. To verify that their individual votes are recorded correctly, voters can look up their ballot serial numbers online and verify that their confirmation codes are posted correctly. The confirmation codes do not allow voters to prove how they voted. However, the confirmation codes constitute convincing evidence of error or malfeasance in the event that incorrect codes are posted online. Correctness of the final tally with respect to the published codes is proven by election officials in a manner that can be verified by any interested party. Thus, compromise of either ballot chain of custody or the software systems cannot undetectably affect election integrity. Scantegrity II has been implemented and tested in small elections in which ballots were scanned either at the polling place or centrally. Preparations for its use in a public sector election have commenced.