7 research outputs found
Usable Secure Private Search
Real-world applications commonly require untrusting parties to share sensitive information securely. This article describes a secure anonymous database search (SADS) system that provides exact keyword match capability. Using a new reroutable encryption and the ideas of Bloom filters and deterministic encryption, SADS lets multiple parties efficiently execute exact-match queries over distributed encrypted databases in a controlled manner. This article further considers a more general search setting allowing similarity searches, going beyond existing work that considers similarity in terms of error tolerance and Hamming distance. This article presents a general framework, built on the cryptographic and privacy-preserving guarantees of the SADS primitive, for engineering usable private secure search systems
Parallel Vectorized Algebraic AES in MATLAB for Rapid Prototyping of Encrypted Sensor Processing Algorithms and Database Analytics
The increasing use of networked sensor systems and networked databases has
led to an increased interest in incorporating encryption directly into sensor
algorithms and database analytics. MATLAB is the dominant tool for rapid
prototyping of sensor algorithms and has extensive database analytics
capabilities. The advent of high level and high performance Galois Field
mathematical environments allows encryption algorithms to be expressed
succinctly and efficiently. This work leverages the Galois Field primitives
found the MATLAB Communication Toolbox to implement a mode of the Advanced
Encrypted Standard (AES) based on first principals mathematics. The resulting
implementation requires 100x less code than standard AES implementations and
delivers speed that is effective for many design purposes. The parallel version
achieves speed comparable to native OpenSSL on a single node and is sufficient
for real-time prototyping of many sensor processing algorithms and database
analytics.Comment: 6 pages; accepted to IEEE High Performance Extreme Computing
Conference (HPEC) 201
Privacy-preserving collaboration in an integrated social environment
Privacy and security of data have been a critical concern at the state, organization and individual levels since times immemorial. New and innovative methods for data storage, retrieval and analysis have given rise to greater challenges on these fronts. Online social networks (OSNs) are at the forefront of individual privacy concerns due to their ubiquity, popularity and possession of a large collection of users' personal data. These OSNs use recommender systems along with their integration partners (IPs) for offering an enriching user experience and growth. However, the recommender systems provided by these OSNs inadvertently leak private user information. In this work, we develop solutions targeted at addressing existing, real-world privacy issues for recommender systems that are deployed across multiple OSNs. Specifically, we identify the various ways through which privacy leaks can occur in a friend recommendation system (FRS), and propose a comprehensive solution that integrates both Differential Privacy and Secure Multi-Party Computation (MPC) to provide a holistic privacy guarantee. We model a privacy-preserving similarity computation framework and library named Lucene-P2. It includes the efficient privacy-preserving Latent Semantic Indexing (LSI) extension. OSNs can use the Lucene-P2 framework to evaluate similarity scores for their private inputs without sharing them. Security proofs are provided under semi-honest and malicious adversary models. We analyze the computation and communication complexities of the protocols proposed and empirically test them on real-world datasets. These solutions provide functional efficiency and data utility for practical applications to an extent.Includes bibliographical references
Private-Key Fully Homomorphic Encryption for Private Classification of Medical Data
A wealth of medical data is inaccessible to researchers and clinicians due to privacy restrictions such as HIPAA. Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients’ privacy. In addition, the medical institutions and companies who own these medical information systems wish to keep their models private when used by outside parties.
Fully homomorphic encryption (FHE) enables practical polynomial computation over encrypted data. This dissertation begins with coverage of speed and security improvements to existing private-key fully homomorphic encryption methods. Next this dissertation presents a protocol for third-party private search using private-key FHE. Finally, fully homomorphic protocols for polynomial machine learning algorithms are presented using privacy-preserving Naive Bayes and Decision Tree classifiers. These protocols allow clients to privately classify their data points without direct access to the learned model. Experiments using these classifiers are run using publicly available medical data sets.
These protocols are applied to the task of privacy-preserving classification of real-world medical data. Results show that private-key fully homomorphic encryption is able to provide fast and accurate results for privacy-preserving medical classification