David J. Wu

Single nucleotide polymorphic discrimination by electronic dot blot assay on semiconductor microchips

We have developed a rapid assay for single nucleotide polymorphism (SNP) detection that utilizes electronic circuitry on silicon microchips. The method was validated by the accurate discrimination of blinded DNA samples for the complex quadra-allelic SNP of mannose binding protein. The microchip directed the transport, concentration, and attachment of amplified patient DNA to selected electrodes (test sites) creating an array of DNA samples. Through control of the electric field, the microchip enabled accurate genetic identification of these samples using fluorescently labeled DNA reporter probes. The accuracy of this approach was established by internal controls of dual labeled reporters and by using mismatched sequences in addition to the wild-type and variant reporter sequences to validate the SNP-genotype. The ability to customize this assay for multiple genes has advantages over other existing approaches.

Text Detection and Character Recognition in Scene Images with Unsupervised Feature Learning

Reading text from photographs is a challenging problem that has received a significant amount of attention. Two key components of most systems are (i) text detection from images and (ii) character recognition, and many recent methods have been proposed to design better feature representations and models for both. In this paper, we apply methods recently developed in machine learning -- specifically, large-scale algorithms for learning the features automatically from unlabeled data -- and show that they allow us to construct highly effective classifiers for both detection and recognition to be used in a high accuracy end-to-end system.

Private database queries using somewhat homomorphic encryption

In a private database query system, a client issues queries to a database and obtains the results without learning anything else about the database and without the server learning the query. While previous work has yielded systems that can efficiently support disjunction queries, performing conjunction queries privately remains an open problem. In this work, we show that using a polynomial encoding of the database enables efficient implementations of conjunction queries using somewhat homomorphic encryption. We describe a three-party protocol that supports efficient evaluation of conjunction queries. Then, we present two implementations of our protocol using Pailliers additively homomorphic system as well as Brakerskis somewhat homomorphic cryptosystem. Finally, we show that the additional homomorphic properties of the Brakerski cryptosystem allow us to handle queries involving several thousand elements over a million-record database in just a few minutes, far outperforming the implementation using the additively homomorphic system.

Practical Order-Revealing Encryption with Limited Leakage

In an order-preserving encryption scheme, the encryption algorithm produces ciphertexts that preserve the order of their plaintexts. Order-preserving encryption schemes have been studied intensely in the last decade, and yet not much is known about the security of these schemes. Very recently, Boneh eti¾?al. Eurocrypti¾?2015 introduced a generalization of order-preserving encryption, called order-revealing encryption, and presented a construction which achieves this notion with best-possible security. Because their construction relies on multilinear maps, it is too impractical for most applications and therefore remains a theoretical result. In this work, we build efficiently implementable order-revealing encryption from pseudorandom functions. We present the first efficient order-revealing encryption scheme which achieves a simulation-based security notion with respect to a leakage function that precisely quantifies what is leaked by the scheme. In fact, ciphertexts in our scheme are only about 1.6 times longer than their plaintexts. Moreover, we show how composing our construction with existing order-preserving encryption schemes results in order-revealing encryption that is strictly more secure than all preceding order-preserving encryption schemes.

Privately Evaluating Decision Trees and Random Forests

Abstract Decision trees and random forests are common classifiers with widespread use. In this paper, we develop two protocols for privately evaluating decision trees and random forests. We operate in the standard two-party setting where the server holds a model (either a tree or a forest), and the client holds an input (a feature vector). At the conclusion of the protocol, the client learns only the model’s output on its input and a few generic parameters concerning the model; the server learns nothing. The first protocol we develop provides security against semi-honest adversaries. We then give an extension of the semi-honest protocol that is robust against malicious adversaries. We implement both protocols and show that both variants are able to process trees with several hundred decision nodes in just a few seconds and a modest amount of bandwidth. Compared to previous semi-honest protocols for private decision tree evaluation, we demonstrate a tenfold improvement in computation and bandwidth.

Dual gene activation and knockout screen reveals directional dependencies in genetic networks

Understanding the direction of information flow is essential for characterizing how genetic networks affect phenotypes. However, methods to find genetic interactions largely fail to reveal directional dependencies. We combine two orthogonal Cas9 proteins from Streptococcus pyogenes and Staphylococcus aureus to carry out a dual screen in which one gene is activated while a second gene is deleted in the same cell. We analyze the quantitative effects of activation and knockout to calculate genetic interaction and directionality scores for each gene pair. Based on the results from over 100,000 perturbed gene pairs, we reconstruct a directional dependency network for human K562 leukemia cells and demonstrate how our approach allows the determination of directionality in activating genetic interactions. Our interaction network connects previously uncharacterized genes to well-studied pathways and identifies targets relevant for therapeutic intervention.

Constraining Pseudorandom Functions Privately

In a constrained pseudorandom function PRF, the master secret key can be used to derive constrained keys, where each constrained keyi¾?k is constrained with respect to some Boolean circuiti¾?C. A constrained keyi¾?k can be used to evaluate the PRF on all inputsi¾?x for which

Function-Hiding Inner Product Encryption Is Practical

Cx = 1