## ProjectsBelow, I describe some of the major themes of my research. Click on the headings for more details. ## Lattice-Based CryptographyLattice-based cryptography is one of the leading candidates for post-quantum cryptography. A major focus of my work has been on constructing new cryptographic primitives such as zero-knowledge proof systems, watermarking, and more, from standard lattice assumptions. ## Proof SystemsA proof system is a two-party protocol between a prover and a verifier, where the goal of the prover is to convince the verifier that some statement is true. In this project, we study and construct new proof systems that satisfy special properties such as zero-knowledge (where we require that the proof does not reveal anything more about the statement other than its truth) and succinctness (where proofs are short and can be verified quickly). ## Watermarking and Traitor TracingA software watermarking scheme enables a user to embed a tag (e.g., a developer's name or a serial number) into a program while preserving the program's functionality. Moreover, it should be difficult to remove the watermark from the resulting program without destroying its functionality. Closely related is the notion of traitor tracing, which are cryptographic schemes that enable users or authorities to trace the source of compromised cryptographic keys and programs. Both of these primitives are useful for protecting against unauthorized use or redistribution of digital content. In this project, we study and propose new constructions of these primitives. ## Genome PrivacyPatient genomes are typically interpretable only in the context of other genomes. However, genome sharing opens individuals up to possible discrimination and identification. Some of my research has focused on developing cryptographic methods to protect the privacy of a patient's genome while still enabling useful computations across multiple genomes. ## Privacy-Preserving SystemsFunctionality and user privacy are often in tension with each other, especially when it comes to modern data-driven and cloud-based applications. Much of my research is on leveraging cryptographic tools and techniques to provide a balance between the need for privacy and the need for functionality. Examples include designing private discovery protocols for the Internet of Things, constructing private navigation systems, and building systems for privacy-preserving machine learning. ## Private Constrained PRFsA constrained pseudorandom function (PRF) is a PRF for which one can generate
constrained keys that can only be used to evaluate the PRF on a subset of the
domain. In this work, we introduce the notion of a ## Order-Revealing EncryptionAn order-revealing encryption (ORE) scheme is an encryption scheme where there is a public function that can be used to compare ciphertexts. Because ORE enables comparisons on ciphertexts, it has many applications in searching over and sorting encrypted data. In this project, we design and implement several practical ORE schemes (based only on pseudorandom functions such as AES). ## Functional EncryptionFunctional encryption (FE) enables fine-grained access control of sensitive
data. In an FE scheme, decryption keys are associated with functions.
Decrypting an encryption of a message ## Fully Homomorphic EncryptionA fully homomorphic encryption system enables computations to be performed on encrypted data without needing to first decrypt the data. In this project, we provide an implementation of Brakerski's scale-invariant somewhat homomorphic encryption (SWHE) system [Bra12]. In addition, we examine several candidate applications of FHE and SWHE systems, such as performing statistical analysis on encrypted data or evaluating private database queries over an encrypted database. ## Text Recognition in Natural ImagesReading text from natural images is a challenging problem that has received significant attention in recent years. Traditional systems in this area have generally relied on elaborate models incorporating carefully hand-engineered features or large amounts of prior knowledge. In this project, we take a different approach and instead, leverage the power of unsupervised feature learning in conjunction with deep, multi-layer neural networks in order to develop robust, high-performing modules for text recognition in natural images. |