Studies in secure computation : post-quantum, attribute-based and multi-party
Abstract/Contents
- Abstract
- The invention of the first public-key cryptosystem four decades ago fired up the development of myriads of secure solutions for protecting data in a large variety of scenarios such as data retrieval, transmission and processing. This dissertation brings together several results on secure computations. We study post-quantum key agreement from generic lattices, attribute-based encryption, and privacy preserving multi-party computations with applications to machine learning. We provide the first instantiation and implementation of a key agreement protocol based on hard problems in random lattices. We show numerous optimizations of the protocol that make it efficient and practical. Next, we build a new lattice-based encryption scheme that lets anyone translate a ciphertext encrypted under a public key x into a ciphertext encrypted under the public key (f(x), f) of the same plaintext for any arithmetic circuit f. We showthat this scheme gives an Attribute-Based Encryption with short keys. Third, we develop systems for privacy preserving data-mining. We design and implement a protocol for computing linear regression model on user data without revealing any other information about the data.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2017 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Nikolaenko, Valeria |
|---|---|
| Associated with | Stanford University, Computer Science Department. |
| Primary advisor | Boneh, Dan |
| Thesis advisor | Boneh, Dan |
| Thesis advisor | Charikar, Moses |
| Thesis advisor | Reingold, Omer |
| Advisor | Charikar, Moses |
| Advisor | Reingold, Omer |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Valeria Nikolaenko. |
|---|---|
| Note | Submitted to the Department of Computer Science. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2017. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2017 by Valeria Olegovna Nikolaenko
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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