Privacy is of the utmost importance in genomic matching. Therefore a number of privacy-preserving protocols have been presented using secure computation. Nevertheless, none of these protocols prevents inferences from the result. Goodrich has shown that this resulting information is sufficient for an effective attack on genome databases. In this paper we present an approach that can detect and mitigate such an attack on encrypted messages while still preserving the privacy of both parties. Note that randomization, e.g. using differential privacy, will almost certainly destroy the utility of the matching result. We combine two known cryptographic primitives -- secure computation of the edit distance and fuzzy commitments -- in order to prevent submission of similar genome sequences. Particularly, we contribute an efficient zero-knowledge proof that the same input has been used in both primitives. We show that using our approach it is feasible to preserve privacy in genome matching and also detect and mitigate Goodrich's attack.
We treat the problem of secure outsourcing of sequence comparisons by a client to remote servers, which given two strings λ and μ of respective lengths n and m, consists of finding a minimum-cost sequence of insertions, deletions, and substitutions (also called an edit script) that transform λ into μ. In our setting a client owns λ and μ and outsources the computation to two servers without revealing to them information about either the input strings or the output sequence. Our solution is non-interactive for the client (who only sends information about the inputs and receives the output) and the client’s work is linear in its input output. The servers’ performance is O(σmn) computation (which is optimal) and communication, where σ is the alphabet size, and the solution is designed to work when the servers have only O(σ(m + n)) memory. By utilizing garbled circuit evaluation in a novel way, we completely avoid public-key cryptography, which makes our solution particularly efficient. To support large-scale biomedical research projects, organizations need to share person-specific genomic sequences without violating the privacy of their data subjects. In the past, organizations protected subjects' identities by removing identifiers, such as name and social security number; however, recent investigations illustrate that deidentified genomic data can be ldquoreidentifiedrdquo to named individuals using simple automated methods. In this paper, we present a novel cryptographic framework that enables organizations to support genomic data mining without disclosing the raw genomic sequences. Organizations contribute encrypted genomic sequence records into a centralized repository, where the administrator can perform queries, such as frequency counts, without decrypting the data. We evaluate the efficiency of our framework with existing databases of single nucleotide polymorphism (SNP) sequences and demonstrate that the time needed to complete count queries is feasible for real world applications. For example, our experiments indicate that a count query over 40 SNPs in a database of 5000 records can be completed in approximately 30 min with off-the-shelf technology. We further show that approximation strategies can be applied to significantly speed up query execution times with minimal loss in accuracy. The framework can be implemented on top of existing information and network technologies in biomedical environments. Large-scale problems in the physical and life sciences are being revolutionized by Internet computing technologies, like grid computing, that make possible the massive cooperative sharing of computational power, bandwidth, storage, and data. A weak computational device, once connected to such a grid, is no longer limited by its slow speed, small amounts of local storage, and limited bandwidth: It can avail itself of the abundance of these resources that is available elsewhere on the network. An impediment to the use of “computational outsourcing” is that the data in question is often sensitive, e.g., of national security importance, or proprietary and containing commercial secrets, or to be kept private for legal requirements such as the HIPAA legislation, Gramm-Leach-Bliley, or similar laws. This motivates the design of techniques for computational outsourcing in a privacy-preserving manner, i.e., without revealing to the remote agents whose computational power is being used, either one's data or the outcome of the computation on the data. This paper investigates such secure outsourcing for widely applicable sequence comparison problems, and gives an efficient protocol for a customer to securely outsource sequence comparisons to two remote agents, such that the agents learn nothing about the customer's two private sequences or the result of the comparison. The local computations done by the customer are linear in the size of the sequences, and the computational cost and amount of communication done by the external agents are close to the time complexity of the best known algorithm for solving the problem on a single machine (i.e., quadratic, which is a huge computational burden for the kinds of massive data on which such comparisons are made). The sequence comparison problem considered arises in a large number of applications, including speech recognition, machine vision, and molecular sequence comparisons. In addition, essentially the same protocol can solve a larger class of problems whose standard dynamic programming solutions are similar in structure to the recurrence that subtends the sequence comparison algorithm. Human Desoxyribo-Nucleic Acid (DNA) sequences offer a wealth of information that reveal, among others, predisposition to various diseases and paternity relations. The breadth and personalized nature of this information highlights the need for privacy-preserving protocols. In this paper, we present a new error-resilient privacy-preserving string searching protocol that is suitable for running private DNA queries. This protocol checks if a short template (e.g., a string that describes a mutation leading to a disease), known to one party, is present inside a DNA sequence owned by another party, accounting for possible errors and without disclosing to each party the other party's input. Each query is formulated as a regular expression over a finite alphabet and implemented as an automaton. As the main technical contribution, we provide a protocol that allows to execute any finite state machine in an oblivious manner, requiring a communication complexity which is linear both in the number of states and the length of the input string. An operation preceding most human DNA analyses is read mapping, which aligns millions of short sequences (called reads) to a reference genome. This step involves an enormous amount of computation (evaluating edit distances for millions upon billions of sequence pairs) and thus needs to be outsourced to low-cost commercial clouds. This asks for scalable techniques to protect sensitive DNA information, a demand that cannot be met by any existing techniques (e.g., homomorphic encryption, secure multiparty computation). In this paper, we report a new step towards secure and scalable read mapping on the hybrid cloud, which includes both the public commercial cloud and the private cloud within an organization. Inspired by the famous “seed-and-extend” method, our approach strategically splits a mapping task: the public cloud seeks exact matches between the keyed hash values of short read substrings (called seeds) and those of reference sequences to roughly position reads on the genome; the private cloud extends the seeds from these positions to find right alignments. Our novel seed-combination technique further moves most workload of this task to the public cloud. The new approach is found to work effectively against known inference attacks, and also easily scale to millions of reads. This work treats the problem of error-resilient DNA searching via oblivious evaluation of finite automata, where a client has a DNA sequence, and a service provider has a pattern that corresponds to a genetic test. Error-resilient searching is achieved by representing the pattern as a finite automaton and evaluating it on the DNA sequence, where privacy of both the pattern and the DNA sequence must be preserved. Interactive solutions to this problem already exist, but can be a burden on the participants. Thus, we propose techniques for secure outsourcing of finite automata evaluation to computational servers, which do not learn any information. Our techniques are applicable to any type of finite automata, but the optimizations are tailored to DNA searching. Many basic tasks in computational biology involve operations on individual DNA and protein sequences. These sequences, even when anonymized, are vulnerable to re-identification attacks and may reveal highly sensitive information about individuals. We present a relatively efficient, privacy-preserving implementation of fundamental genomic computations such as calculating the edit distance and Smith- Waterman similarity scores between two sequences. Our techniques are crypto graphically secure and significantly more practical than previous solutions. We evaluate our prototype implementation on sequences from the Pfam database of protein families, and demonstrate that its performance is adequate for solving real-world sequence-alignment and related problems in a privacy- preserving manner. Furthermore, our techniques have applications beyond computational biology. They can be used to obtain efficient, privacy-preserving implementations for many dynamic programming algorithms over distributed datasets.