UnLynx: A Decentralized System for Privacy-Conscious Data Sharing

Abstract

Current solutions for privacy-preserving data sharing among multiple parties either depend on a centralized authority that must be trusted and provides only weakest-link security (e.g., the entity that manages private/secret cryptographic keys), or leverage on decentralized but impractical approaches (e.g., secure multi-party computation). When the data to be shared are of a sensitive nature and the number of data providers is high, these solutions are not appropriate. Therefore, we present UnLynx, a new decentralized system for efficient privacypreserving data sharing. We consider m servers that constitute a collective authority whose goal is to verifiably compute on data sent from n data providers. UnLynx guarantees the confidentiality, unlinkability between data providers and their data, privacy of the end result and the correctness of computations by the servers. Furthermore, to support differentially private queries, UnLynx can collectively add noise under encryption. All of this is achieved through a combination of a set of new distributed and secure protocols that are based on homomorphic cryptography, verifiable shuffling and zero-knowledge proofs. UnLynx is highly parallelizable and modular by design as it enables multiple security/privacy vs. runtime tradeoffs. Our evaluation shows that UnLynx can execute a secure survey on 400,000 personal data records containing 5 encrypted attributes, distributed over 20 independent databases, for a total of 2,000,000 ciphertexts, in 24 minutes.

Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets

“…Systems relying on homomorphic encryption [11], [13], [16], [27], [28], [29] are often limited in the functionalities they offer (e.g., sum only). They present high-performance overhead in comparison with their less secure counterparts or still rely on honest-but-curious parties.…”

“…They present high-performance overhead in comparison with their less secure counterparts or still rely on honest-but-curious parties. In our previous work, we presented UnLynx [16], a decentralized system that enables the computation of (only) sums on distributed datasets and ensures DP s' privacy and data confidentiality. UnLynx assumes DP s to be honest-but-curious and, unlike Drynx, it does not ensure end results robustness.…”

“…Nevertheless, many of these systems rely on honest-but-curious or trusted third-party assumptions that might not provide sufficient guarantees when the data to be shared are highly sensitive, valuable, influential or private. Other solutions with stronger threat models, e.g., UnLynx [16], are limited in the computations they support, e.g., sum only. Moreover, none of these solutions considers the possibility that both computing entities and data providers can be malicious.…”

“…• We propose and implement an efficient, modular and multi-functionality query-execution pipeline by introducing Collective Tree Obfuscation, a new distributed protocol that enables a collective and verifiable obfuscation of encrypted data; presenting multiple data-encoding techniques that enable distributed computations of advanced statistics on homomorphically encrypted data. We propose new encodings, and improvements and adaptations of previously introduced private-aggregation encodings to our framework and security model; adapting an existing zero-knowledge scheme for input-range validation to our security model; proposing a new construction of the Key Switching protocol introduced in UnLynx [16], improving both its performance and capabilities. To the best of our knowledge, Drynx is the only operational system that provides the aforementioned security and privacy guarantees.…”

Clinical sequencing: From raw data to diagnosis with lifetime value

“…With ever‐increasing amounts of genetic data there are emerging regulatory issues regarding data safety and storage. Although cloud environments enable high‐capacity data storage (eg, Amazon Web Services, https://aws.amazon.com/), there are legitimate concerns about the privacy and security of sensitive cloud‐stored genetic data, indicating the need for more secure and decentralized solutions (eg, UnLynx) . Data sharing, which facilitates genotype‐phenotype correlation, is subject to further discussions as it has been shown that even after de‐identification, re‐identification of a single person is at least partially possible by either STR or SNP genotyping …”