Order-preserving encryption allows encrypting data, while still enabling efficient range queries on the encrypted data. This makes its performance and functionality very suitable for data outsourcing in cloud computing scenarios, but the security of order-preserving is still debatable. We present a scheme that achieves a strictly stronger notion of security than any other scheme so far. The basic idea is to randomize the ciphertexts to hide the frequency of plaintexts. Still, the client storage size remains small, in our experiments up to 1/15 of the plaintext size. As a result, one can more securely outsource large data sets, since we can also show that our security increases with larger data sets.

We give an efficient protocol for sequence comparisons of the edit-distance kind, such that neither party reveals anything about their private sequence to the other party (other than what can be inferred from the edit distance between their two sequences -which is unavoidable because computing that distance is the purpose of the protocol). The amount of communication done by our protocol is proportional to the time complexity of the best-known algorithm for performing the sequence comparison.The problem of determining the similarity between two sequences arises in a large number of applications, in particular in bioinformatics. In these application areas, the edit distance is one of the most widely used notions of sequence similarity: It is the least-cost set of insertions, deletions, and substitutions required to transform one string into the other. The generalizations of edit distance that are solved by the same kind of dynamic programming recurrence relation as the one for edit distance, cover an even wider domain of applications.

Order-preserving encryption enables performing many classes of queries -including range queries -on encrypted databases. Popa et al. recently presented an ideal-secure order-preserving encryption (or encoding) scheme, but their cost of insertions (encryption) is very high. In this paper we present an also ideal-secure, but significantly more efficient orderpreserving encryption scheme. Our scheme is inspired by Reed's referenced work on the average height of random binary search trees. We show that our scheme improves the average communication complexity from O(n log n) to O(n) under uniform distribution. Our scheme also integrates efficiently with adjustable encryption as used in CryptDB. In our experiments for database inserts we achieve a performance increase of up to 81% in LANs and 95% in WANs.

Searchable (symmetric) encryption allows encryption while still enabling search for keywords. Its immediate application is cloud storage where a client outsources its files while the (cloud) service provider should search and selectively retrieve those. Searchable encryption is an active area of research and a number of schemes with different efficiency and security characteristics have been proposed in the literature. Any scheme for practical adoption should be efficient -i.e. have sub-linear search time -, dynamic -i.e. allow updates -and semantically secure to the most possible extent. Unfortunately, efficient, dynamic searchable encryption schemes suffer from various drawbacks. Either they deteriorate from semantic security to the security of deterministic encryption under updates, they require to store information on the client and for deleted files and keywords or they have very large index sizes. All of this is a problem, since we can expect the majority of data to be later added or changed. Since these schemes are also less efficient than deterministic encryption, they are currently an unfavorable choice for encryption in the cloud. In this paper we present the first searchable encryption scheme whose updates leak no more information than the access pattern, that still has asymptotically optimal search time, linear, very small and asymptotically optimal index size and can be implemented without storage on the client (except the key). Our construction is based on the novel idea of learning the index for efficient access from the access pattern itself. Furthermore, we implement our system and show that it is highly efficient for cloud storage.

No abstract