CORE

Cryptocurrency platforms facilitate trade, currency exchange, payments and other advanced financial instruments for Bitcoin and other emerging cryptocurrencies. Although there has been an explosion of innovation over the past several years, frequent security lapses threaten the viability of these new technologies. This project investigates how cybersecurity shocks affect the operation of cryptocurrency platforms. It has three main parts.

Many big-data workloads are hosted on the cloud to facilitate sharing and low cost. Many of these workloads deal with sensitive data, e.g., electronic health records. Cloud infrastructures are vulnerable to attacks from untrusted operators with physical access to the computers. These attacks can take many forms and may compromise privacy or integrity. To guarantee the security of sensitive data and ensure the cost-effectiveness of shared cyberinfrastructure, it is vital that these attacks be thwarted while not incurring a significant penalty in terms of performance, energy, or cost.

Individuals and organizations can frequently benefit from combining their data to learn collective models. However, combining data to enable multi-party learning is often not possible. It may not be permitted due to privacy policies, or may be considered too risky for a business to expose its own data to others. In addition, high-dimensional data are prevalent in modern data-driven applications. Learning from high-dimensional data owned by differential organizations is even more challenging, due to the bias introduced by the high-dimensional machine learning methods.

The collection, retention, and misuse of private information can pose threats to individuals, institutions, and collective values. Cryptography can be a useful tool to help address these concerns, enhancing our security and our ability to feel secure. Historically, however, this potential benefit has been largely unrealized, perhaps because most of the research in cryptography have gone to securing electronic commerce and to investigating foundational questions in the field.

Virtual and augmented reality (VR/AR) applications are expected to play an increasingly important role in many aspects of everyday life; however, we do not yet have effective methods for protecting VR/AR systems from cybersecurity threats. The goal of this research is to make VR/AR systems more secure via the development of highly accurate and counterfeit-resistant biometric techniques based on eye movements. These techniques are based on the computational modeling of multiple characteristics of the way individuals move their eyes.