Existing secure data outsourcing systems offer users ways to select from different cryptographic primitives supported by the system to encrypt their data to strike a balance between data confidentiality and query performance. Though prior work have identified the danger of mixing cryptographic primitives, they fall short of providing a systematic approach to guide users to prevent such cross-cryptographic leakages. Inspired by the database design theory, we envision Secure Normal Form, a new approach to normalize encrypted databases such that the leakages of the partitioned databases are limited to the users' specifications. In this work, we propose a new architecture to support secure normal form. This system includes several new components for secure data outsourcing: (i) an inference mechanism that reasons about additional leakages from weaker encryption techniques, based on semantic data properties (e.g., dependence between attribute values); (ii) a normalization mechanism that converts relational data into secure normal forms, so that the information leaked by the representation is limited to that specified by the user; and (iii) a secure query execution approach over encrypted data in secure normal forms. Our initial experimental results validate the performance improvement over naïve baseline and show that a careful data representation can be allowed without compromising security. We believe that our paper opens a new direction in secure data management.

Simply restricting the computation to non-sensitive part of the data may lead to inferences on sensitive data through data dependencies. Prior work on preventing inference control through data dependencies detect and deny queries which may lead to leakage, or only protect against exact reconstruction of the sensitive data. These solutions result in poor utility, and poor security respectively. In this paper, we present a novel security model calledfull deniability. Under this stronger security model, any information inferred about sensitive data from non-sensitive data is considered as a leakage. We describe algorithms for efficiently implementing full deniability on a given database instance with a set of data dependencies and sensitive cells. Using experiments on two different datasets, we demonstrate that our approach protects against realistic adversaries while hiding only minimal number of additional non-sensitive cells and scales well with database size and sensitive data.

Recent years have witnessed the adoption of differential privacy (DP) in practical database systems like PINQ, FLEX, and PrivateSQL. Such systems allow data analysts to query sensitive data while providing a rigorous and provable privacy guarantee. However, the existing design of these systems does not distinguish data analysts of different privilege levels or trust levels. This design can have an unfair apportion of the privacy budget among the data analyst if treating them as a single entity, or waste the privacy budget if considering them as non-colluding parties and answering their queries independently. In this paper, we propose DProvDB, a fine-grained privacy provenance framework for the multi-analyst scenario that tracks the privacy loss to each single data analyst. Under this framework, when given a fixed privacy budget, we build algorithms that maximize the number of queries that could be answered accurately and apportion the privacy budget according to the privilege levels of the data analysts.

We study the problem of answering queries when (part of) the data may be sensitive and should not be leaked to the querier. Simply restricting the computation to non-sensitive part of the data may leak sensitive data through inference based on data dependencies. While inference control from data dependencies during query processing has been studied in the literature, existing solution either detect and deny queries causing leakage, or use a weak security model that only protects against exact reconstruction of the sensitive data. In this paper, we adopt a stronger security model based on full deniability that prevents any information about sensitive data to be inferred from query answers. We identify conditions under which full deniability can be achieved and develop an efficient algorithm that minimally hides non-sensitive cells during query processing to achieve full deniability. We experimentally show that our approach is practical and scales to increasing proportion of sensitive data, as well as, to increasing database size.