Managed Blockchain Based Cryptocurrencies with Consensus Enforced Rules and Transparency

Blockchain based cryptocurrencies are usually unmanaged, distributed, consensus-based systems in which no single entity has control. Managed cryptocurrencies can be implemented using private blockchains but are fundamentally different as the owners have complete control to do arbitrary activity without transparency (since they control the mining). In this work we explore a hybrid approach where a managed cryptocurrency is maintained through distributed consensus based methods. The currency administrator can perform ongoing management functions while the consensus methods enforce the rules of the cryptocurrency and provide transparency for all management actions. This enables the introduction of money management features common in fiat currencies but where the managing entity cannot perform arbitrary actions and transparency is enforced. We thus eliminate the need for users to trust the currency administrator but also to enable the administrator to manage the cryptocurrency. We demonstrate how to implement our approach through modest modifications to the implicit Bitcoin specification, however, our approach can be applied to most any blockchain based cryptocurrency using a variety of consensus methods.Comment: 10 pages, 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communication

Measurements of the Most Significant Software Security Weaknesses

In this work, we provide a metric to calculate the most significant software security weaknesses as defined by an aggregate metric of the frequency, exploitability, and impact of related vulnerabilities. The Common Weakness Enumeration (CWE) is a well-known and used list of software security weaknesses. The CWE community publishes such an aggregate metric to calculate the `Most Dangerous Software Errors'. However, we find that the published equation highly biases frequency and almost ignores exploitability and impact in generating top lists of varying sizes. This is due to the differences in the distributions of the component metric values. To mitigate this, we linearize the frequency distribution using a double log function. We then propose a variety of other improvements, provide top lists of the most significant CWEs for 2019, provide an analysis of the identified software security weaknesses, and compare them against previously published top lists.Comment: 12 page