The aim of the IACR Ph.D. database is twofold. On the first hand, we want to offer an overview of Ph.D. already completed
in the domain of cryptology. Where possible, this should also include a subject classification, an abstract, and
access to the full text.
On the second hand, it deals with Ph.D. subjects
currently under investigation. This way, we provide a timely
map of contemporary research in cryptology.
All entries or changes need to be approved by an editor. You can contact them via phds (at) iacr.org.
Dan Bogdanov (#869)
Topic of his/her doctorate.
Sharemind: programmable secure computations with practical applications
secret sharing, secure multiparty computation, applications
Year of completion
Imagine the leader of a state who wants to make wise choices on how to use the nation’s budget and also wants to know, how these decisions pay off. For this, the leader needs data from the citizens and the companies. Often, this data is private to a person (like financial status and health) or a business secret to a company. In a modern society, there are limits on how much a government can learn about its subjects before the power given by knowing too much starts to erode the freedom of the people.
The goal of this work is to allow sensitive data to be processed while preserving the confidentiality of the data owner. We achieve this by using secure multiparty computation. Secure multiparty computation is a cryptographic technique that allows digital information to be processed without letting the person who is doing the processing see the values or associate them with their source. We can use this technology to collect data, analyze it and publish the aggregated result without compromising the privacy of the people.
The thesis introduces Sharemind – a framework for creating secure data processing applications. Sharemind is based on new secure multiparty computation protocol suite that can be efficiently executed on current computing technology. The thesis discusses the security guarantees that Sharemind provides and measures its performance on digital computers.
The secure computation protocols of Sharemind can be freely reordered to calculate many statistical functions or to evaluate more complex algorithms on the data. The thesis presents SecreC – a programming language for simplifying the use of Sharemind in applications. Sharemind has been used for building several research prototypes that demonstrate privacy preserving statistics and data mining techniques. In addition, Sharemind has been used to implement the first real-world secure multiparty computation application that worked using the public internet. The application has been used for financial reporting by the Estonian Association of Information Technology and Telecommunications.
The methods described in this thesis can help both the government and companies in securely processing confidential information.
dan (at) cyber.ee