In permissioned blockchain systems, participants are admitted to the network by receiving a credential from a certification authority. Each transaction processed by the network is required to be authorized by a valid participant who authenticates via their credential. Use case settings where privacy is a concern thus require proper privacy-preserving authentication and authorization mechanisms.
Anonymous credential schemes are cryptographic mechanisms that allow a user to authenticate while showing only those attributes necessary in a given setting, which makes these schemes a great tool for authorizing transactions in permissioned blockchain systems based on the user's attributes. As in most setups of such systems there is one distinct certification authority for each organization in the network, the use of plain anonymous credential schemes still leaks the association of a user to their issuing organization. Camenisch, Drijvers, and Dubovitskaya (CCS 2017) therefore suggest the use of delegatable anonymous credential schemes, which allows to hide even that remaining piece of information.
We implement private transaction authorization in Hyperledger Fabric based on delegatable anonymous credentials. To this end, we provide a production-grade open-source implementation of the Camenisch et al. scheme with several optimizations. We then extend Fabric to support the scheme as an additional mechanism for authorizing transactions. Our solution supports revocation and auditing, making it ready for real-world deployment. Our performance measurements show that the scheme, while incurring an overhead in comparison to the less privacy-preserving ones, is practical for settings with enhanced privacy requirements.
Motivation: The complexity of protein-protein interactions (PPIs) is further compounded by the fact that an average protein consists of two or more domains, structurally and evolutionary independent subunits. Experimental studies have demonstrated that an interaction between a pair of proteins is not carried out by all domains constituting each protein, but rather by a select subset. However, finding which domains from each protein mediate the corresponding PPI is a challenging task. Results: Here, we present Domain Interaction Statistical POTential (DISPOT), a simple knowledge-based statistical potential that estimates the propensity of an interaction between a pair of protein domains, given their SCOP family annotations. The statistical potential is derived based on the analysis of more than 352,000 structurally resolved protein-protein interactions obtained from DOMMINO, a comprehensive database on structurally resolved macromolecular interactions. Availability and implementation: DISPOT is implemented in Python 2.7 and packaged as an open-source tool. DISPOT is implemented in two modes, basic and auto-extraction. The source code for both modes is available on GitHub: github.com/korkinlab/dispot and standalone docker images on DockerHub: hub.docker.com/r/korkinlab/dispot. The web-server is freely available at dispot.korkinlab.org.
Database query evaluation over encrypted data has received a lot of attention recently. Order Preserving Encryption (OPE) and Order Revealing Encryption (ORE) are two important encryption schemes that have been proposed in this area. These schemes can provide very efficient query execution but at the same time may leak some information to adversaries. In this paper, we present the first comprehensive comparison among a number of important OPE and ORE schemes using a framework that we developed. We evaluate protocols that are based on these schemes as well. We analyze and compare them both theoretically and experimentally and measure their performance over database indexing and query evaluation techniques using not only execution time but also IO performance and usage of cryptographic primitive operations. Our comparison reveals some interesting insights concerning the relative security and performance of these approaches in database settings. Furthermore, we propose a number of improvements for some of these scheme and protocols. Finally, we provide a number of suggestions and recommendations that can be valuable to database researchers and users.
I present a dynamic, voluntary contribution mechanism, public good game and derive its potential outcomes. In each period, players endogenously determine contribution productivity by engaging in costly investment. The level of contribution productivity carries from period to period, creating a dynamic link between periods. The investment mimics investing in the stock of technology for producing public goods such as national defense or a clean environment. After investing, players decide how much of their remaining money to contribute to provision of the public good, as in traditional public good games. I analyze three kinds of outcomes of the game: the lowest payoff outcome, the Nash Equilibria, and socially optimal behavior. In the lowest payoff outcome, all players receive payoffs of zero. Nash Equilibrium occurs when players invest any amount and contribute all or nothing depending on the contribution productivity. Therefore, there are infinitely many Nash Equilibria strategies. Finally, the socially optimal result occurs when players invest everything in early periods, then at some point switch to contributing everything. My goal is to discover and explain this point. I use mathematical analysis and computer simulation to derive the results.
This project expands the functionality of the Massachusetts Technology, Talent, and Economic Reporting System (MATTERS) for the Massachusetts High Technology Council (MHTC), a protechnology advocacy and lobbyist organization, through the addition of two new features, namely, an Application Program Interface (API) and the Metric Builder. This API defines a communication protocol between MATTERS and other computational-based systems. Extensive API documentation was developed. The Metric Builder is a tool that allows users to create their own indices with custom rules out of existing MATTERS metrics. This empowers them to track individual states' performance using their own custom models.
The purpose of this IQP project is to scientifically develop profitable systems and indicators for trading in the markets. The project consists of nine individually developed strategies, which were quantitatively analyzed for profitability and then combined into a system of systems. Each individual system or indicator was given defined rules and then allocated simulated money to trade. Two types of systems were mainly developed, predictive and confirmative, leading to a system of systems that incorporated a predictive layer and a confirmative layer in the decision to take positions.