With the many benefits of cloud computing, an entity may want to outsource its data and their related analytics tasks to a cloud. When data are sensitive, it is in the interest of the entity to outsource encrypted data to the cloud; however, this limits the types of operations that can be performed on the cloud side. Especially, evaluating queries over the encrypted data stored on the cloud without the entity performing any computation and without ever decrypting the data become a very challenging problem. In this paper, we propose solutions to conduct range queries over outsourced encrypted data. The existing methods leak valuable information to the cloud which can violate the security guarantee of the underlying encryption schemes. In general, the main security primitive used to evaluate range queries is secure comparison (SC) of encrypted integers. However, we observe that the existing SC protocols are not very efficient. To this end, we first propose a novel SC scheme that takes encrypted integers and outputs encrypted comparison result. We empirically show its practical advantage over the current state-of-the-art. We then utilize the proposed SC scheme to construct two new secure range query protocols. Our protocols protect data confidentiality, privacy of user's query, and also preserve the semantic security of the encrypted data; therefore, they are more secure than the existing protocols. Furthermore, our second protocol is lightweight at the user end, and it can allow an authorized user to use any device with limited storage and computing capability to perform the range queries over outsourced encrypted data.
In this paper, we do not consider the existing secure comparison protocols that are secure under the information theoretic setting. This is because, the existing secure comparison protocols under the information theoretic setting are commonly based on linear secret sharing schemes, such as Shamir's @cite_10 , which require at least three parties. We emphasize that our problem setting is entirely different than those methods since the data in our case are encrypted and our protocols require only two parties. Our protocols, which are based on additive homomorphic encryption schemes, are orthogonal to the secret sharing based SC schemes. Nevertheless, developing a PPRQ protocol by using the secret sharing based SC methods that protect the data access patterns is still an open problem; therefore, it can be treated as an interesting future work.
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2082624086 2570972465 2046270839 2143087446
Many deployments of secure multi-party computation (MPC) in practice have used information-theoretic three-party protocols that tolerate a single, semi-honest corrupt party, since these protocols enjoy very high efficiency. We propose a new approach for secure three-party computation (3PC) that improves security while maintaining practical efficiency that is competitive with traditional information-theoretic protocols. Our protocol is based on garbled circuits and provides security against a single, malicious corrupt party. Unlike information-theoretic 3PC protocols, ours uses a constant number of rounds. Our protocol only uses inexpensive symmetric-key cryptography: hash functions, block ciphers, pseudorandom generators (in particular, no oblivious transfers) and has performance that is comparable to that of Yao's (semi-honest) 2PC protocol. We demonstrate the practicality of our protocol with an implementation based on the JustGarble framework of (S&P 2013). The implementation incorporates various optimizations including the most recent techniques for efficient circuit garbling. We perform experiments on several benchmarking circuits, in different setups. Our experiments confirm that, despite providing a more demanding security guarantee, our protocol has performance comparable to existing information-theoretic 3PC. We consider a problem where multiple agents participate in solving a quadratic optimization problem subject to linear inequality constraints in a privacy-preserving manner. Several variables of the objective function as well as the constraints are privacy-sensitive and are known to different agents. We propose a privacy-preserving protocol based on partially homomorphic encryption where each agent encrypts its own information before sending it to an untrusted cloud computing infrastructure. To find the optimal solution the cloud applies a gradient descent algorithm on the encrypted data without the ability to decrypt it. The privacy of the proposed protocol against coalitions of colluding agents is analyzed using the cryptography notion of zero knowledge proofs. We are interested in the design of automated procedures for analyzing the (in)security of cryptographic protocols in the Dolev-Yao model for a bounded number of sessions when we take into account some algebraic properties satisfied by the operators involved in the protocol. This leads to a more realistic model in comparison to what we get under the perfect cryptography assumption, but it implies that protocol analysis deals with terms modulo some equational theory instead of terms in a free algebra. The main goal of this paper is to setup a general approach that works for a whole class of monoidal theories which contains many of the specific cases that have been considered so far in an ad-hoc way (e.g. exclusive or, Abelian groups, exclusive or in combination with the homomorphism axiom). We follow a classical schema for cryptographic protocol analysis which proves first a locality result and then reduces the insecurity problem to a symbolic constraint solving problem. This approach strongly relies on the correspondence between a monoidal theory E and a semiring S"E which we use to deal with the symbolic constraints. We show that the well-defined symbolic constraints that are generated by reasonable protocols can be solved provided that unification in the monoidal theory satisfies some additional properties. The resolution process boils down to solving particular quadratic Diophantine equations that are reduced to linear Diophantine equations, thanks to linear algebra results and the well-definedness of the problem. Examples of theories that do not satisfy our additional properties appear to be undecidable, which suggests that our characterization is reasonably tight. We consider the problem of computing the intersection of private datasets of two parties, where the datasets contain lists of elements taken from a large domain. This problem has many applications for online collaboration. We present protocols, based on the use of homomorphic encryption and balanced hashing, for both semi-honest and malicious environments. For lists of length k, we obtain O(k) communication overhead and O(k ln ln k) computation. The protocol for the semi-honest environment is secure in the standard model, while the protocol for the malicious environment is secure in the random oracle model. We also consider the problem of approximating the size of the intersection, show a linear lower-bound for the communication overhead of solving this problem, and provide a suitable secure protocol. Lastly, we investigate other variants of the matching problem, including extending the protocol to the multi-party setting as well as considering the problem of approximate matching.