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Register a new userPSI ({Psi}): a Private data Sharing Interface
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Added by
Jack Murtagh
Publication date
2016
fields
Informatics Engineering
and research's language is
English
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We provide an overview of PSI (a Private data Sharing Interface), a system we are developing to enable researchers in the social sciences and other fields to share and explore privacy-sensitive datasets with the strong privacy protections of differential privacy.
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In this work we describe a token-based solution to Contact Tracing via Distributed Point Functions (DPF) and, more generally, Function Secret Sharing (FSS). The key idea behind the solution is that FSS natively supports secure keyword search on raw sets of keywords without a need for processing the keyword sets via a data structure for set membership. Furthermore, the FSS functionality enables adding up numerical payloads associated with multiple matches without additional interaction. These features make FSS an attractive tool for lightweight privacy-preserving searching on a database of tokens belonging to infected individuals.
Permissioned blockchain such as Hyperledger fabric enables a secure supply chain model in Industrial Internet of Things (IIoT) through multichannel and private data collection mechanisms. Sharing of Industrial data including private data exchange at every stage between supply chain partners helps to improve product quality, enable future forecast, and enhance management activities. However, the existing data sharing and querying mechanism in Hyperledger fabric is not suitable for supply chain environment in IIoT because the queries are evaluated on actual data stored on ledger which consists of sensitive information such as business secrets, and special discounts offered to retailers and individuals. To solve this problem, we propose a differential privacy-based permissioned blockchain using Hyperledger fabric to enable private data sharing in supply chain in IIoT (DH-IIoT). We integrate differential privacy into the chaindcode (smart contract) of Hyperledger fabric to achieve privacy preservation. As a result, the query response consists of perturbed data which protects the sensitive information in the ledger. The proposed work (DH-IIoT) is evaluated by simulating a permissioned blockchain using Hyperledger fabric. We compare our differential privacy integrated chaincode of Hyperledger fabric with the default chaincode setting of Hyperledger fabric for supply chain scenario. The results confirm that the proposed work maintains 96.15% of accuracy in the shared data while guarantees the protection of sensitive ledgers data.
Multisite medical data sharing is critical in modern clinical practice and medical research. The challenge is to conduct data sharing that preserves individual privacy and data usability. The shortcomings of traditional privacy-enhancing technologies mean that institutions rely on bespoke data sharing contracts. These contracts increase the inefficiency of data sharing and may disincentivize important clinical treatment and medical research. This paper provides a synthesis between two novel advanced privacy enhancing technologies (PETs): Homomorphic Encryption and Secure Multiparty Computation (defined together as Multiparty Homomorphic Encryption or MHE). These PETs provide a mathematical guarantee of privacy, with MHE providing a performance advantage over separately using HE or SMC. We argue MHE fulfills legal requirements for medical data sharing under the General Data Protection Regulation (GDPR) which has set a global benchmark for data protection. Specifically, the data processed and shared using MHE can be considered anonymized data. We explain how MHE can reduce the reliance on customized contractual measures between institutions. The proposed approach can accelerate the pace of medical research whilst offering additional incentives for healthcare and research institutes to employ common data interoperability standards.
In the fight against Covid-19, many governments and businesses are in the process of evaluating, trialling and even implementing so-called immunity passports. Also known as antibody or health certificates, there is a clear demand for any technology that could allow people to return to work and other crowded places without placing others at risk. One of the major criticisms of such systems is that they could be misused to unfairly discriminate against those without immunity, allowing the formation of an `immuno-privileged class of people. In this work we are motivated to explore an alternative technical solution that is non-discriminatory by design. In particular we propose health tokens -- randomised health certificates which, using methods from differential privacy, allow individual test results to be randomised whilst still allowing useful aggregate risk estimates to be calculated. We show that health tokens could mitigate immunity-based discrimination whilst still presenting a viable mechanism for estimating the collective transmission risk posed by small groups of users. We evaluate the viability of our approach in the context of identity-free and identity-binding use cases and then consider a number of possible attacks. Our experimental results show that for groups of size 500 or more, the error associated with our method can be as low as 0.03 on average and thus the aggregated results can be useful in a number of identity-free contexts. Finally, we present the results of our open-source prototype which demonstrates the practicality of our solution.
Motivated by a recent successful dynamical explanation for the newly observed fully-charm structure $X(6900)$ in the mass spectrum of di-$J/psi$ by LHCb [J.~Z.~Wang textit{et al.} arXiv:2008.07430], in this work, we extend the same dynamical rescattering mechanism to predict the line shape of more potential fully-heavy structures in the invariant mass spectrum of $J/psi psi(3686)$, $J/psi psi(3770)$, $psi(3686) psi(3686)$, and $J/psi Upsilon(1S)$ at high energy proton-proton collisions, whose verification in experiments should be helpful to further clarify the nature of $X(6900)$. The above final states of vector heavy quarkonia can be experimentally reconstructed more effectively by a $mu^+mu^-$ pair in the muon detector compared with $Qbar{Q}$ meson with other quantum numbers. Furthermore, the corresponding peak mass positions of each of predicted fully-heavy structures are also given. Our theoretical studies here could provide some valuable information for the future measurement proposals of LHCb and CMS, especially based on the accumulated data after completing Run III of LHC in the near future.
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