Practical Private Range Search in Depth

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on “practical” private range search (mainly in database venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees or permit unacceptable privacy leakages. In this article, we take an interdisciplinary approach, which combines the rigor of security formulations and proofs with efficient data management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE), primarily proposed for keyword search. We reduce range search to multi-keyword search using range-covering techniques with tree-like indexes, and formalize the problem as Range Searchable Symmetric Encryption (RSSE). We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure and (ii) minimizing false positives incurred by some schemes under heavy data skew. We also explain an important concept in the recent SSE bibliography, namely locality, and design generic and specialized ways to attribute locality to our RSSE schemes. Moreover, we are the first to devise secure schemes for answering range aggregate queries, such as range sums and range min/max. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

Practical Private Range Search Revisited

Ioannis Demertzis, Odysseas Papapetrou

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on "practical" private range search (mainly in Databases venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees, or permit unacceptable privacy leakages. In this paper, we take an interdisciplinary approach, which combines the rigor of Security formulations and proofs with efficient Data Management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE) primarily proposed for keyword search. We reduce range search to multi-keyword search using range covering techniques with tree-like indexes. We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure, and (ii) minimizing false positives incurred by some schemes under heavy data skew. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

2016

Data-centric trust in ephemeral networks

Maxime Raya

New network types require new security concepts. Surprisingly, trust – the ultimate goal of security – has not evolved as much as other concepts. In particular, the traditional notion of building trust in entities seems inadequate in an ephemeral environment where contacts among nodes are often short-lived and non-recurrent. It is actually the trustworthiness of the data that entities generate that matters most in these ephemeral networks. And what makes things more interesting is the continuous "humanization" of devices, by making them reflect more closely their owners' preferences, including the human sense of costs. Hence, in this thesis we study the notion of data-centric trust in an ephemeral network of rational nodes. The definition of a new notion requires specifying the corresponding basis, measures, and raison d'être. In the following chapters, we address these issues. We begin by defining the system and security models of an example ephemeral network, namely a vehicular network. Next, we delve into the subject of revocation in vehicular networks, before creating and analyzing a game-theoretic model of revocation, where the notion of cost-aware devices makes its first appearance in this thesis. This model not only makes possible the comparison of different revocation mechanisms in the literature, but also leads to the design of an optimal solution, the RevoGame protocol. With the security architecture in place, we formally define data-centric trust and compare several mechanisms for evaluating it. Notably, we apply the Dempster-Shafer Theory to cases of high uncertainty. Last but not least, we show that data-centric trust can reduce the privacy loss resulting from the need to establish trust. We first create a model of the trust-privacy tradeoff and then analyze it with game theory, in an environment of privacy-preserving entities. Our analysis shows that proper incentives can achieve this elusive tradeoff.

EPFL2009

Privacy-Enhancing Technologies for Medical and Genomic Data: From Theory to Practice

Jean Louis Raisaro

The impressive technological advances in genomic analysis and the significant drop in the cost of genome sequencing are paving the way to a variety of revolutionary applications in modern healthcare. In particular, the increasing understanding of the human genome, and of its relation to diseases, health and to responses to treatments brings promise of improvements in better preventive and personalized medicine. Unfortunately, the impact on privacy and security is unprecedented. The genome is our ultimate identifier and, if leaked, it can unveil sensitive and personal information such as our genetic diseases, our propensity to develop certain conditions (e.g., cancer or Alzheimer's) or the health issues of our family. Even though legislation, such as the EU General Data Protection Regulation (GDPR) or the US Health Insurance Portability and Accountability Act (HIPAA), aims at mitigating abuses based on genomic and medical data, it is clear that this information also needs to be protected by technical means. In this thesis, we investigate the problem of developing new and practical privacy-enhancing technologies (PETs) for the protection of medical and genomic data. Our goal is to accelerate the adoption of PETs in the medical field in order to address the privacy and security concerns that prevent personalized medicine from reaching its full potential. We focus on two main areas of personalized medicine: clinical care and medical research. For clinical care, we first propose a system for securely storing and selectively retrieving raw genomic data that is indispensable for in-depth diagnoses and treatments of complex genetic diseases such as cancer. Then, we focus on genetic variants and devise a new model based on additively-homomorphic encryption for privacy-preserving genetic testing in clinics. Our model, implemented in the context of HIV treatment, is the first to be tested and evaluated by practitioners in a real operational setting. For medical research, we first propose a method that combines somewhat-homomorphic encryption with differential privacy to enable secure feasibility studies on genetic data stored at an untrusted central repository. Second, we address the problem of sharing genomic and medical data when the data is distributed across multiple mistrustful institutions. We begin by analyzing the risks that threaten patientsâ privacy in systems for the discovery of genetic variants, and we propose practical mitigations to the re-identification risk. Then, for clinical sites to be able to share the data without worrying about the risk of data breaches, we develop a new system based on collective homomorphic encryption: it achieves trust decentralization and enables researchers to securely find eligible patients for clinical studies. Finally, we design a new framework, complementary to the previous ones, for quantifying the risk of unintended disclosure caused by potential inference attacks that are jointly combined by a malicious adversary, when exact genomic data is shared. In summary, in this thesis we demonstrate that PETs, still believed unpractical and immature, can be made practical and can become real enablers for overcoming the privacy and security concerns blocking the advancement of personalized medicine. Addressing privacy issues in healthcare remains a great challenge that will increasingly require long-term collaboration among geneticists, healthcare providers, ethicists, lawmakers, and computer scientists.