Towards a secure and efficient search over encrypted cloud data

Includes bibliographical references.2016 Summer.Cloud computing enables new types of services where the computational and network resources are available online through the Internet. One of the most popular services of cloud computing is data outsourcing. For reasons of cost and convenience, public as well as private organizations can now outsource their large amounts of data to the cloud and enjoy the benefits of remote storage and management. At the same time, confidentiality of remotely stored data on untrusted cloud server is a big concern. In order to reduce these concerns, sensitive data, such as, personal health records, emails, income tax and financial reports, are usually outsourced in encrypted form using well-known cryptographic techniques. Although encrypted data storage protects remote data from unauthorized access, it complicates some basic, yet essential data utilization services such as plaintext keyword search. A simple solution of downloading the data, decrypting and searching locally is clearly inefficient since storing data in the cloud is meaningless unless it can be easily searched and utilized. Thus, cloud services should enable efficient search on encrypted data to provide the benefits of a first-class cloud computing environment. This dissertation is concerned with developing novel searchable encryption techniques that allow the cloud server to perform multi-keyword ranked search as well as substring search incorporating position information. We present results that we have accomplished in this area, including a comprehensive evaluation of existing solutions and searchable encryption schemes for ranked search and substring position search

Storage Efficient Substring Searchable Symmetric Encryption

We address the problem of substring searchable encryption. A single user produces a big stream of data and later on wants to learn the positions in the string that some patterns occur. Although current techniques exploit auxiliary data structures to achieve efficient substring search on the server side, the cost at the user side may be prohibitive. We revisit the work of substring searchable encryption in order to reduce the storage cost of auxiliary data structures. Our solution entails a suffix array based index design, which allows optimal storage cost O (n) with small hidden factor at the size of the string n. We analyze the security of the protocol in the real ideal framework. Moreover, we implemented our scheme and the state of the art protocol [7] to demonstrate the performance advantage of our solution with precise benchmark results

Obfuscating Finite Automata

We construct a VBB and perfect circuit-hiding obfuscator for evasive deterministic finite automata using a matrix encoding scheme with a limited zero-testing algorithm. We construct the matrix encoding scheme by extending an existing matrix FHE scheme. Using obfuscated DFAs we can for example evaluate secret regular expressions or disjunctive normal forms on public inputs. In particular, the possibility of evaluating regular expressions solves the open problem of obfuscated substring matching

Substring-Searchable Symmetric Encryption

In this paper, we consider a setting where a client wants to outsource storage of a large amount of private data and then perform substring search queries on the data -- given a data string s and a search string p, find all occurrences of p as a substring of s. First, we formalize an encryption paradigm that we call queryable encryption, which generalizes searchable symmetric encryption (SSE) and structured encryption. Then, we construct a queryable encryption scheme for substring queries. Our construction uses suffix trees and achieves asymptotic efficiency comparable to that of unencrypted suffix trees. Encryption of a string of length n takes O(kn) time and produces a ciphertext of size O(kn), and querying for a substring of length m that occurs z times takes O(km+z) time and three rounds of communication, where k is the security parameter. Our security definition guarantees correctness of query results and privacy of data and queries against a malicious, adaptive adversary. Following the line of work started by Curtmola et al. (ACM CCS 2006), in order to construct more efficient schemes we allow the query protocol to leak some limited information that is captured precisely in the definition. We prove security of our substring-searchable encryption scheme against malicious adversaries, where the query protocol leaks limited information about memory access patterns through the suffix tree of the encrypted string

Reuse It Or Lose It: More Efficient Secure Computation Through Reuse of Encrypted Values

Two-party secure function evaluation (SFE) has become significantly more feasible, even on resource-constrained devices, because of advances in server-aided computation systems. However, there are still bottlenecks, particularly in the input validation stage of a computation. Moreover, SFE research has not yet devoted sufficient attention to the important problem of retaining state after a computation has been performed so that expensive processing does not have to be repeated if a similar computation is done again. This paper presents PartialGC, an SFE system that allows the reuse of encrypted values generated during a garbled-circuit computation. We show that using PartialGC can reduce computation time by as much as 96% and bandwidth by as much as 98% in comparison with previous outsourcing schemes for secure computation. We demonstrate the feasibility of our approach with two sets of experiments, one in which the garbled circuit is evaluated on a mobile device and one in which it is evaluated on a server. We also use PartialGC to build a privacy-preserving "friend finder" application for Android. The reuse of previous inputs to allow stateful evaluation represents a new way of looking at SFE and further reduces computational barriers.Comment: 20 pages, shorter conference version published in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, Pages 582-596, ACM New York, NY, US

Generalized closest substring encryption

We propose a new cryptographic notion called generalized closest substring encryption. In this notion, a ciphertext encrypted with a string S can be decrypted with a private key of another string S′, if there exist a substring of S, i.e. S^, and a substring of S′, i.e. S^′, that are close to each other measured by their overlap distance . The overlap distance between S^ and S^′ is the number of identical positions at which the corresponding symbols are the same. In comparison with other encryption systems, the closest notion is the Fuzzy-IBE proposed by Sahai and Waters. The main difference is that the Fuzzy-IBE measures the overlap distance between S and S′, while ours measures the overlap distance of all of their substrings (including the complete string), and we take the maximum value among those. The overlap distance between their substrings will measure the similarity of S and S′ more precisely compared to the overlap distance between the two complete strings. We note that embedding this overlap distance in an encryption is a challenging task, in particular in order to achieve a practical scheme. Therefore, we invent a new approach to develop a practical generalized closest substring encryption system. The novelty of our approach relies on the way we generate ciphertext and private key representing the complete string so that they can still measure the overlap distance of substrings. The size of ciphertext and private key grow linearly only in the length of the input string. We prove the security in the selective model under a generalization of decision q-Bilinear Diffie-Hellman Exponent assumption

Automata Evaluation and Text Search Protocols with Simulation Based Security

This paper presents efficient protocols for securely computing the following two problems: 1) The fundamental problem of pattern matching. This problem is defined in the two-party setting, where party $P_1$ holds a pattern and party $P_2$ holds a text. The goal of $P_1$ is to learn where the pattern appears in the text, without revealing it to $P_2$ or learning anything else about $P_2$\u27s text. This problem has been widely studied for decades due to its broad applicability. We present several protocols for several notions of security. We further generalize one of our solutions to solve additional pattern matching related problems of interest. 2) Our construction from above, in the malicious case, is based on a novel protocol for secure oblivious automata evaluation which is of independent interest. In this problem, party $P_1$ holds an automaton and party $P_2$ holds an input string, and they need to decide if the automaton accepts the input, without learning anything else. Our protocol obtains full security in the face of malicious adversaries

How (Not) to Index Order Revealing Encrypted Databases

Order Reveling Encryption (ORE) enables efficient range queries on encrypted databases, but may leak information that could be exploited by inference attacks. State-of-the-art ORE schemes claim different security guarantees depending on the adversary attack surface. Intuitively, online adversaries who access the database server at runtime may access information leakage; offline adversaries who access only a snapshot of the database data should not be able to gain useful information. We focus on offline security of the ORE scheme proposed by Lewi and Wu (LW-ORE, CCS 2016), which guarantees semantic security of ciphertexts stored in the database, but requires that ciphertexts are maintained sorted with regard to the corresponding plaintexts to support sublinear time queries. The design of LW-ORE does not discuss how to build indexing data structures to maintain sorting. The risk is that practitioners consider indexes as a technicality whose design does not affect security. We show that indexes can affect offline security of LW-ORE because they may leak duplicate plaintext values, and statistical information on plaintexts distribution and on transactions history. As a real-world demonstration, we found two open source implementations related to academic research (JISA 2018, VLDB 2019), and both adopt standard search trees which may introduce such vulnerabilities. We discuss necessary conditions for indexing data structures to be secure for ORE databases, and we outline practical solutions. Our analyses could represent an insightful lesson in the context of security failures due to gaps between theoretical modeling and actual implementation, and may also apply to other cryptographic techniques for securing outsourced databases

Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Rapid advances in human genomics are enabling researchers to gain a better understanding of the role of the genome in our health and well-being, stimulating hope for more effective and cost efficient healthcare. However, this also prompts a number of security and privacy concerns stemming from the distinctive characteristics of genomic data. To address them, a new research community has emerged and produced a large number of publications and initiatives. In this paper, we rely on a structured methodology to contextualize and provide a critical analysis of the current knowledge on privacy-enhancing technologies used for testing, storing, and sharing genomic data, using a representative sample of the work published in the past decade. We identify and discuss limitations, technical challenges, and issues faced by the community, focusing in particular on those that are inherently tied to the nature of the problem and are harder for the community alone to address. Finally, we report on the importance and difficulty of the identified challenges based on an online survey of genome data privacy expertsComment: To appear in the Proceedings on Privacy Enhancing Technologies (PoPETs), Vol. 2019, Issue

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

Machine learning techniques are an excellent tool for the medical community to analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent the free sharing of this data. Encryption methods such as fully homomorphic encryption (FHE) provide a method evaluate over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for private prediction using medical data. FHE has also been shown to enable secure genomic algorithms, such as paternity testing, and secure application of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced. Details on current open-source implementations are provided, as is the state of FHE for privacy-preserving techniques in machine learning and bioinformatics and future growth opportunities for FHE