Strengthening the Security of Encrypted Databases: Non-Transitive JOINs

Database management systems operating over encrypted data are gaining significant commercial interest. CryptDB is one such notable system supporting a variety SQL queries over encrypted data (Popa et al., SOSP \u2711). It is a practical system obtained by utilizing a number of encryption schemes, together with a new cryptographic primitive for supporting SQL\u27s join operator. This new primitive, an adjustable join scheme, is an encoding scheme that enables to generate tokens corresponding to any two database columns for computing their join given only their encodings. Popa et al. presented a framework for modeling the security of adjustable join schemes, but it is not completely clear what types of potential adversarial behavior it captures. Most notably, CryptDB\u27s join operator is transitive, and this may reveal a significant amount of sensitive information. In this work we put forward a strong and intuitive notion of security for adjustable join schemes, and argue that it indeed captures the security of such schemes: We introduce, in addition, natural simulation-based and indistinguishability-based notions (capturing the ``minimal leakage\u27\u27 of such schemes), and prove that our notion is positioned between their adaptive and non-adaptive variants. Then, we construct an adjustable join scheme that satisfies our notion of security based on the linear assumption (or on the seemingly stronger matrix-DDH assumption for improved efficiency) in bilinear groups. Instantiating CryptDB with our scheme strengthens its security by providing a non-transitive join operator, while increasing the size of CryptDB\u27s encodings from one group element to four group elements based on the linear assumption (or two group elements based on the matrix-DDH assumption), and increasing the running time of the adjustment operation from that of computing one group exponentiation to that of computing four bilinear maps based on the linear assumption (or two bilinear maps based on the matrix-DDH assumption). Most importantly, however, the most critical and frequent operation underlying our scheme is comparison of single group elements as in CryptDB\u27s join scheme

Protecting information privacy in the electronic society

The privacy of users, the confidentiality of organizations, and the protection of huge collections of sensitive information, possibly related to data that might be released publicly or semi-publicly for various purposes, are essential requirements for the today\u2019s Electronic Society. In this chapter, we discuss the main privacy concerns that arise when releasing information to third parties. In particular, we focus on the data publication and data outsourcing scenarios, illustrating the emerging trends in terms of privacy and data protection and identifying some research directions to be investigated

Privacy-Preserving Complex Query Evaluation over Semantically Secure Encrypted Data

In the last decade, several techniques have been proposed to evaluate different types of queries (e.g., range and aggregate queries) over encrypted data in a privacy-preserving manner. However, solutions supporting the privacy-preserving evaluation of complex queries over encrypted data have been developed only recently. Such recent techniques, however, are either insecure or not feasible for practical applications. In this paper, we propose a novel privacy-preserving query processing framework that supports complex queries over encrypted data in the cloud computing environment and addresses the shortcomings of previous approaches. At a high level, our framework utilizes both homomorphic encryption and garbled circuit techniques at different stages in query processing to achieve the best performance, while at the same time protecting the confidentiality of data, privacy of the user’s input query and hiding data access patterns. Also, as a part of query processing, we provide an efficient approach to systematically combine the predicate results (in encrypted form) of a query to derive the corresponding query evaluation result in a privacy-preserving manner. We theoretically and empirically analyze the performance of this approach and demonstrate its practical value over the current state-of-the-art techniques. Our proposed framework is very efficient from the user’s perspective, thus allowing a user to issue queries even using a resource constrained device (e.g., PDAs and cell phones

Encryption and Fragmentation for Data Confidentiality in the Cloud

Cloud computing has emerged as a successful paradigm allowing individual users as well as companies to resort to external providers for storing/processing data or making them available to others. Together with the many benefits, cloud computing introduces however new security and privacy risks. A major issue is that the data owner, storing data at external providers, loses control over them, leaving them potentially exposed to improper access, use, or dissemination. In this chapter, we consider the problem of protecting confidentiality of sensitive information when relying on external cloud providers for storing and processing data. We introduce confidentiality requirements and then illustrate encryption and data fragmentation as possible protection techniques. In particular, we discuss different approaches that have been proposed using encryption (with indexing) and fragmentation, either by themselves or in combination, to satisfy confidentiality requirements

Enforcing Privacy in Cloud Databases

Outsourcing databases, i.e., resorting to Database-as-a-Service (DBaaS), is nowadays a popular choice due to the elasticity, availability, scalability and pay-as-you-go features of cloud computing. However, most data are sensitive to some extent, and data privacy remains one of the top concerns to DBaaS users, for obvious legal and competitive reasons.In this paper, we survey the mechanisms that aim at making databases secure in a cloud environment, and discuss current pitfalls and related research challenges