arXiv subjects

Dmytro Bogatov

Publications and source records attributed to Dmytro Bogatov.

3 recordsLinked to original sources

arXiv2023

Analysis of a Dynamic Voluntary Contribution Mechanism Public Good Game

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.

econ.EM↗

arXiv2022

Secure and Efficient Query Processing in Outsourced Databases

Various cryptographic techniques are used in outsourced database systems to ensure data privacy while allowing for efficient querying. This work proposes a definition and components of a new secure and efficient outsourced database system, which answers various types of queries, with different privacy guarantees in different security models. This work starts with the survey of five order-revealing encryption schemes that can be used directly in many database indices and five range query protocols with various security / efficiency tradeoffs. The survey systematizes the state-of-the-art range query solutions in a snapshot adversary setting and offers some non-obvious observations regarding the efficiency of the constructions. In $\mathcal{E}\text{psolute}$, a secure range query engine, security is achieved in a setting with a much stronger adversary where she can continuously observe everything on the server, and leaking even the result size can enable a reconstruction attack. $\mathcal{E}\text{psolute}$ proposes a definition, construction, analysis, and experimental evaluation of a system that provably hides both access pattern and communication volume while remaining efficient. The work concludes with $k\text{-a}n\text{o}n$ -- a secure similarity search engine in a snapshot adversary model. The work presents a construction in which the security of $k\text{NN}$ queries is achieved similarly to OPE / ORE solutions -- encrypting the input with an approximate Distance Comparison Preserving Encryption scheme so that the inputs, the points in a hyperspace, are perturbed, but the query algorithm still produces accurate results. We use TREC datasets and queries for the search, and track the rank quality metrics such as MRR and nDCG. For the attacks, we build an LSTM model that trains on the correlation between a sentence and its embedding and then predicts words from the embedding.

cs.CR↗

arXiv2021

$\mathcal{E}\text{psolute}$: Efficiently Querying Databases While Providing Differential Privacy

As organizations struggle with processing vast amounts of information, outsourcing sensitive data to third parties becomes a necessity. To protect the data, various cryptographic techniques are used in outsourced database systems to ensure data privacy, while allowing efficient querying. A rich collection of attacks on such systems has emerged. Even with strong cryptography, just communication volume or access pattern is enough for an adversary to succeed. In this work we present a model for differentially private outsourced database system and a concrete construction, $\mathcal{E}\text{psolute}$, that provably conceals the aforementioned leakages, while remaining efficient and scalable. In our solution, differential privacy is preserved at the record level even against an untrusted server that controls data and queries. $\mathcal{E}\text{psolute}$ combines Oblivious RAM and differentially private sanitizers to create a generic and efficient construction. We go further and present a set of improvements to bring the solution to efficiency and practicality necessary for real-world adoption. We describe the way to parallelize the operations, minimize the amount of noise, and reduce the number of network requests, while preserving the privacy guarantees. We have run an extensive set of experiments, dozens of servers processing up to 10 million records, and compiled a detailed result analysis proving the efficiency and scalability of our solution. While providing strong security and privacy guarantees we are less than an order of magnitude slower than range query execution of a non-secure plain-text optimized RDBMS like MySQL and PostgreSQL.

cs.CR↗