Every year the amount of digitally stored sensitive information increases significantly. Information such as governmental and legal documents, health, and tax records are required to be securely archived for decades to comply with various laws and regulations. Since cryptographic schemes based on single computational assumptions are not guaranteed to stay secure for such long periods, current state-of-the-art systems providing long-term confidentiality and integrity rely on information-theoretic techniques, such as multi-server secret sharing and commitments. These systems achieve the desired results; however, establishing private channels for secret sharing is costly and requires a complex setup. In this paper, we present ArchiveSafe LT, a framework for archiving systems aiming to provide long-term confidentiality and integrity. The framework relies on multiple computationally-secure schemes using robust combiners, with a design that plans for agility and evolution of cryptographic schemes. ArchiveSafe LT is efficient and suitable for practical adoption as it eliminates the need for private channels compared to its counterparts. We present the ArchiveSafe LT framework structure and its security analysis using an automatic prover. We specify two ArchiveSafe LT-based system designs, which handle different adversarial storage providers. We experimentally evaluate a prototype built based on one of the designs to show the system’s efficiency compared to information-theoretic systems.

Deep learning models are increasingly being used to predict patients’ diagnoses by analyzing electronic health records. Medical records represent observations of a patient’s health over time. A commonly used approach to analyze health records is to encode them as a sequence of ordered diagnoses (diagnostic-level encoding). Transformer models then analyze the sequence of diagnoses to learn disease patterns. However, the elapsed time between medical visits is not considered when transformers are used to analyze health records. In this paper, we present DT-THRE: Decoder Transformer for Temporally-Embedded Health Records Encoding that predicts patients’ diagnoses by analyzing their medical histories. In DTTHRE, instead of diagnostic-level encoding, we propose an encoding representation for health records called THRE: Temporally-Embedded Health Records Encoding. THRE encodes patient histories as a sequence of medical events such as age, sex, and diagnostic embedding while incorporating the elapsed time between visits. We evaluate a proof-of-concept DTTHRE on a real-world medical dataset and compare our model’s performance to an existing diagnostic transformer model in the literature. DTTHRE was successful on a medical dataset to predict patients’ final diagnosis with improved predictive performance (78.54± 0.22%) compared to the existing model in the literature (40.51± 0.13%).

Autonomous Medical Advisory Systems (AMAS) integrate sensors and implement learning technologies to provide intelligent and real-time recommendations. In this paper, we propose a formal framework for quantifying trust using the Bayesian network for the sensor layer of AMAS systems. First, we identify the various factors influencing trust in this context. We make the factors granular enough such that the probability of the trust for the factor to be in a specific state can be measured. Then, using a probabilistic graphical model, we impose a compact structure to the identified factors such that the posterior probability of the trustworthiness of the entire system or its constituents can be computed. Parameterized cases of Bayesian network are simulated in MATLAB to demonstrate the applicability and scalability of the model for trust inference.

An increasing number of security breaches in North America are the result of stolen or weak credentials yet many businesses have not adapted their user authentication strategies to account for this vulnerability. This paper presents a preliminary study on a purely statistical keystroke dynamics authentication system that provides an additional layer of security on top of traditional username and password authentication. This form of authentication will reduce the threat of stolen or weak credentials for virtually any system which uses a standard keyboard for authentication. Our model produced an FRR and FAR as low as 2.54% and 0% respectively which is an improvement over other statistical keystroke dynamics authentication models.

In this paper, we introduce a machine learning predictive model that detects patterns of security vulnerabilities in smart contracts. We adapted two static code analyzers to label more than 1000 smart contracts that were verified and used on the Ethereum platform. Our model predicted a number of major software vulnerabilities with the average accuracy of 95 percent. The model currently supports smart contracts developed in Solidity, however, the approach described in this paper can be applied to other languages and blockchain platforms.

Department of Computing and Software at Mc-Master University and Highmark Global are collaborating on a research project to develop an efficient blockchain-based marketplace for software testing. In this paper, we propose a consensus protocol for the blockchain platform in medical related technologies. The proposed consensus protocol incorporates a software testing algorithm that takes into account the capability and credibility of software testers and temporally adjusts these qualities based on the performance of testing jobs completed by the testers. In addition, the protocol maintains a quantifiable measure of testers' capabilities based on the past jobs they have completed. We expect to implement this protocol and deploy on a blockchain platform to automate the software testing validation process and resolve discrepancies that arise between testing results from different testers.

Privacy audit logs are used to capture the actions of participants in a data sharing environment in order for auditors to check compliance with privacy policies. However, collusion may occur between the auditors and participants to obfuscate actions that should be recorded in the audit logs. In this paper, we propose a Linked Data based method of utilizing blockchain technology to create tamper-proof audit logs that provide proof of log manipulation and non-repudiation.

In this paper, we propose an architecture that utilizes blockchain technology for enabling verifiable trust in collaborative health research environments. The architecture supports the human-in-the-loop paradigm for health research by establishing trust between participants, including human researchers and AI systems, by making all data transformations transparent and verifiable by all participants. We define the trustworthiness of the system and provide an analysis of the architecture in terms of trust requirements. We then evaluate our architecture by analyzing its resiliency to common security threats and through an experimental realization.

In this paper, a secure and efficient message authentication protocol is introduced which uses a combination of digital signature and symmetric key encryption to support message integrity, authentication, and non-repudiation in VANETs. In this model a new role for the RSU is defined for verifying the authenticity of messages sent from vehicles and for notifying the results back to vehicles. Extensive simulations are conducted to validate that the proposed scheme outperforms the protocols that are solely rely on the public key infrastructure for message authentication and non-repudiation.

We describe a framework designed to facilitate privacy auditing while accommodating a variety of privacy scenarios and policies that involve multiple participants. Our proposal is based on two ontologies, L2TAP and SCIP, designed for deployment in a Linked Data environment. L2TAP provides provenance

The Internet and related technologies have created enormous potential for disruptive innovations. Businesses engaging in e-commerce must constantly be examining opportunities and threats arising from disruptive change. Modeling techniques have been introduced to help visualize and reason about business models and strategies. This paper offers a modeling approach which characterizes a business model not in terms of flows or exchanges, but the strategic dependencies among various players. The business model is then analyzed in relation to the high-level strategy of the business. When a change arises, competitive scenarios are analyzed in terms of the strategic choices for the incumbent and new entrants. A historical case study from the telecom sector is used to illustrate.