Blockchain-based audit trails provide a consensus-driven and tamper-proof trail of system events that are helpful in creating provenance in enterprise solutions. However, taking into account the transaction bulk generated by these applications and the throughput limitations of existing blockchains, a single ledger for record keeping can be inefficient and costly. To that end, we see an imperative need for a new blockchain design that is capable of addressing current challenges, without compromising security and provenance. Hence, we propose BlockTrail, a scalable and efficient blockchain solution for auditing applications. BlockTrail fragments the legacy blockchain systems into layers of co-dependent hierarchies, thereby reducing the time and space complexity, and increasing the throughput. BlockTrail is prototyped on "Practical Byzantine Fault Tolerance" (PBFT) protocol with a custom-built blockchain. Experiments with BlockTrail show that compared to the conventional schemes, BlockTrail is more efficient, and has less storage footprint.

Audit logs serve as a critical component in the enterprise business systems that are used for auditing, storing, and tracking changes made to the data. However, audit logs are vulnerable to a series of attacks, which enable adversaries to tamper data and corresponding audit logs. In this paper, we present BlockAudit: a scalable and tamper-proof system that leverages the design properties of audit logs and security guarantees of blockchains to enable secure and trustworthy audit logs. Towards that, we construct the design schema of BlockAudit, and outline its operational procedures. We implement our design on Hyperledger and evaluate its performance in terms of latency, network size, and payload size. Our results show that conventional audit logs can seamlessly transition into BlockAudit to achieve higher security, integrity, and fault tolerance.

MOTIVATION: Discovery of clinically relevant disease sub-types is of prime importance in personalized medicine. Disease sub-type identification has in the past often been explored in an unsupervised machine learning paradigm which involves clustering of patients based on available-omics data, such as gene expression. A follow-up analysis involves determining the clinical relevance of the molecular sub-types such as that reflected by comparing their disease progressions. The above methodology, however, fails to guarantee the separability of the sub-types based on their subtype-specific survival curves. RESULTS: We propose a new algorithm, Survival-based Bayesian Clustering (SBC) which simultaneously clusters heterogeneous-omics and clinical end point data (time to event) in order to discover clinically relevant disease subtypes. For this purpose we formulate a novel Hierarchical Bayesian Graphical Model which combines a Dirichlet Process Gaussian Mixture Model with an Accelerated Failure Time model. In this way we make sure that patients are grouped in the same cluster only when they show similar characteristics with respect to molecular features across data types (e.g. gene expression, mi-RNA) as well as survival times. We extensively test our model in simulation studies and apply it to cancer patient data from the Breast Cancer dataset and The Cancer Genome Atlas repository. Notably, our method is not only able to find clinically relevant sub-groups, but is also able to predict cluster membership and survival on test data in a better way than other competing methods. AVAILABILITY AND IMPLEMENTATION: Our R-code can be accessed as https://github.com/ashar799/SBC. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.