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Secure and Practical Functional Dependency Discovery in Outsourced Databases

Xinle Cao, Yuhan Li, Dmytro Bogatov, Jian Liu, Kui Ren
Conference / Journal paper Under review Cryptology ePrint Archive | 2023


The popularity of cloud computing has made outsourced databases prevalent in real-world applications. To protect data security, numerous encrypted outsourced databases have been proposed for this paradigm. However, the maintenance of encrypted databases has scarcely been addressed. In this paper, we focus on a typical maintenance task — functional dependency (FD) discovery. We develop novel FD protocols in encrypted databases while guaranteeing minimal leakages: nothing is revealed besides the database size and the actual discovered FDs. As far as we know, we are the first to formally define secure FD discovery with minimal leakage.

We present two oblivious FD protocols and prove them secure in the presence of the persistent adversary (monitoring processes on the server). The first protocol leverages Oblivious RAM (ORAM) and is suitable for dynamic databases. The second protocol relies on oblivious sorting and is more practical in static databases due to high parallelism. We also present a thorough experimental evaluation of the proposed methods.

DISPOT: A simple knowledge-based protein domain interaction statistical potential

Oleksandr Narykov, Dmytro Bogatov, Dmitry Korkin
Conference / Journal paper Bioinformatics | 2019 | DOI: 10.1093/bioinformatics/btz587


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: and standalone docker images on DockerHub: The web-server is freely available at

Analysis of a Dynamic Voluntary Contribution Mechanism Public Good Game

Dmytro Bogatov
Conference / Journal paper Academia Work Issues in Political Economy | 26(1), 116-133, 2017 | DOI: 10.48550/arXiv.1807.04621


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.

Data MATTERS: Customizing Economic Indices to Measure State Competitiveness

Dmytro Bogatov, Jillian Rose Hennessy
Academia Work WPI Library | 2016


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.

Investment Trading And Risk Management: Scientifically Developing and Analyzing Trading Systems

Batyrlan Nurbekov, Dmytro Bogatov, Jiacong S. Xu, Richard Joseph O'Brien
Academia Work WPI Library | 2015


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.