Efficient Techniques for Privacy-Preserving Sharing of Sensitive Information

The need for privacy-preserving sharing of sensitive information occurs in many different and realistic everyday scenarios, ranging from national security to social networking. A typical setting involves two parties: one seeks information from the other without revealing the interest while the latter is either willing, or compelled, to share only the requested information. This poses two challenges: (1) how to enable sharing such that parties learn no information beyond what they are entitled to, and (2) how to do so efficiently, in real-world practical terms. This paper explores the notion of Privacy-Preserving Sharing of Sensitive Information (PPSSI), and provides a concrete and efficient instantiation, modeled in the context of simple database querying. Proposed approach functions as a privacy shield to protect parties from disclosing more than the required minimum of their respective sensitive information. PPSSI deployment prompts several challenges, which are addressed in this paper. Extensive experimental results attest to the practicality of attained privacy features and show that our approach incurs quite low overhead (e.g., 10% slower than standard MySQL). © 2011 Springer-Verlag

Distributed Searchable Symmetric Encryption

Searchable Symmetric Encryption (SSE) allows a client to store encrypted data on a storage provider in such a way, that the client is able to search and retrieve the data selectively without the storage provider learning the contents of the data or the words being searched for. Practical SSE schemes usually leak (sensitive) information during or after a query (e.g., the search pattern). Secure schemes on the other hand are not practical, namely they are neither efficient in the computational search complexity, nor scalable with large data sets. To achieve efficiency and security at the same time, we introduce the concept of distributed SSE (DSSE), which uses a query proxy in addition to the storage provider.\ud We give a construction that combines an inverted index approach (for efficiency) with scrambling functions used in private information retrieval (PIR) (for security). The proposed scheme, which is entirely based on XOR operations and pseudo-random functions, is efficient and does not leak the search pattern. For instance, a secure search in an index over one million documents and 500 keywords is executed in less than 1 second

Selective and private access to outsourced data centers

The advancements in the Information Technology and the rapid diffusion of novel computing paradigms have accelerated the trend of moving data to the cloud. Public and private organizations are more often outsourcing their data centers to the cloud for economic and/or performance reasons, thus making data confidentiality an essential requirement. A basic technique for protecting data confidentiality relies on encryption: data are encrypted by the owner before their outsourcing. Encryption however complicates both the query evaluation and enforcement of access restrictions to outsourced data. In this chapter, we provide an overview of the issues and techniques related to the support of selective and private access to outsourced data in a scenario where the cloud provider is trusted for managing the data but not for reading their content. We therefore illustrate methods for enforcing access control and for efficiently and privately executing queries (at the server side) over encrypted data. We also show how the combined adoption of approaches supporting access control and for efficient query evaluation may cause novel privacy issues that need to be carefully handled

Private-Key Fully Homomorphic Encryption for Private Classification of Medical Data

A wealth of medical data is inaccessible to researchers and clinicians due to privacy restrictions such as HIPAA. Clinicians would benefit from access to predictive models for diagnosis, such as classification of tumors as malignant or benign, without compromising patients’ privacy. In addition, the medical institutions and companies who own these medical information systems wish to keep their models private when used by outside parties. Fully homomorphic encryption (FHE) enables practical polynomial computation over encrypted data. This dissertation begins with coverage of speed and security improvements to existing private-key fully homomorphic encryption methods. Next this dissertation presents a protocol for third-party private search using private-key FHE. Finally, fully homomorphic protocols for polynomial machine learning algorithms are presented using privacy-preserving Naive Bayes and Decision Tree classifiers. These protocols allow clients to privately classify their data points without direct access to the learned model. Experiments using these classifiers are run using publicly available medical data sets. These protocols are applied to the task of privacy-preserving classification of real-world medical data. Results show that private-key fully homomorphic encryption is able to provide fast and accurate results for privacy-preserving medical classification

Practical Private Information Retrieval

In recent years, the subject of online privacy has been attracting much interest, especially as more Internet users than ever are beginning to care about the privacy of their online activities. Privacy concerns are even prompting legislators in some countries to demand from service providers a more privacy-friendly Internet experience for their citizens. These are welcomed developments and in stark contrast to the practice of Internet censorship and surveillance that legislators in some nations have been known to promote. The development of Internet systems that are able to protect user privacy requires private information retrieval (PIR) schemes that are practical, because no other efficient techniques exist for preserving the confidentiality of the retrieval requests and responses of a user from an Internet system holding unencrypted data. This thesis studies how PIR schemes can be made more relevant and practical for the development of systems that are protective of users' privacy. Private information retrieval schemes are cryptographic constructions for retrieving data from a database, without the database (or database administrator) being able to learn any information about the content of the query. PIR can be applied to preserve the confidentiality of queries to online data sources in many domains, such as online patents, real-time stock quotes, Internet domain names, location-based services, online behavioural profiling and advertising, search engines, and so on. In this thesis, we study private information retrieval and obtain results that seek to make PIR more relevant in practice than all previous treatments of the subject in the literature, which have been mostly theoretical. We also show that PIR is the most computationally efficient known technique for providing access privacy under realistic computation powers and network bandwidths. Our result covers all currently known varieties of PIR schemes. We provide a more detailed summary of our contributions below: Our first result addresses an existing question regarding the computational practicality of private information retrieval schemes. We show that, unlike previously argued, recent lattice-based computational PIR schemes and multi-server information-theoretic PIR schemes are much more computationally efficient than a trivial transfer of the entire PIR database from the server to the client (i.e., trivial download). Our result shows the end-to-end response times of these schemes are one to three orders of magnitude (10--1000 times) smaller than the trivial download of the database for realistic computation powers and network bandwidths. This result extends and clarifies the well-known result of Sion and Carbunar on the computational practicality of PIR. Our second result is a novel approach for preserving the privacy of sensitive constants in an SQL query, which improves substantially upon the earlier work. Specifically, we provide an expressive data access model of SQL atop of the existing rudimentary index- and keyword-based data access models of PIR. The expressive SQL-based model developed results in between 7 and 480 times improvement in query throughput than previous work. We then provide a PIR-based approach for preserving access privacy over large databases. Unlike previously published access privacy approaches, we explore new ideas about privacy-preserving constraint-based query transformations, offline data classification, and privacy-preserving queries to index structures much smaller than the databases. This work addresses an important open problem about how real systems can systematically apply existing PIR schemes for querying large databases. In terms of applications, we apply PIR to solve user privacy problem in the domains of patent database query and location-based services, user and database privacy problems in the domain of the online sales of digital goods, and a scalability problem for the Tor anonymous communication network. We develop practical tools for most of our techniques, which can be useful for adding PIR support to existing and new Internet system designs