Optimizing the workflow of a complex organization such as a hospital is a difficult task. An accurate option is to use a real-time locating system to track locations of both patients and staff. However, privacy regulations forbid hospital management to assess location data of their staff members. In this exploratory work, we propose a secure solution to analyze the joined location data of patients and staff, by means of an innovative cryptographic technique called Secure Multi-Party Computation, in which an additional entity that the staff members can trust, such as a labour union, takes care of the staff data. The hospital, owning location data of patients, and the labour union perform a two-party protocol, in which they securely cluster the staff members by means of the frequency of their patient facing times. We describe the secure solution in detail, and evaluate the performance of our proof-of-concept. This work thus demonstrates the feasibility of secure multi-party clustering in this setting.

Background: HIV treatment prescription is a complex process. Clinical decision support systems (CDSS) are a category of health information technologies that can assist clinicians to choose optimal treatments based on clinical trials and expert knowledge. The usability of some CDSSs for HIV treatment would be significantly improved by using the knowledge obtained by treating other patients. This knowledge, however, is mainly contained in patient records, whose usage is restricted due to privacy and confidentiality constraints.Methods: A treatment effectiveness measure, containing valuable information for HIV treatment prescription, was defined and a method to extract this measure from patient records was developed. This method uses an advanced cryptographic technology, known as secure Multiparty Computation (henceforth referred to as MPC), to preserve the privacy of the patient records and the confidentiality of the clinicians’ decisions.Findings: Our solution enables to compute an effectiveness measure of an HIV treatment, the average time-to-treatment-failure, while preserving privacy. Experimental results show that our solution, although at proof-of-concept stage, has good efficiency and provides a result to a query within 24 min for a dataset of realistic size.Interpretation: This paper presents a novel and efficient approach HIV clinical decision support systems, that harnesses the potential and insights acquired from treatment data, while preserving the privacy of patient records and the confidentiality of clinician decisions.

In the model that has become known as “Perfectly Secure Message Transmission” (PSMT), a sender Alice is connected to a receiver Bob through n parallel two-way channels. A computationally unbounded adversary Eve controls t of these channels, meaning she can acquire and alter any data that is transmitted over these channels. The sender Alice wishes to communicate a secret message to Bob privately and reliably, i.e. in such a way that Eve gains no information about the message while Bob is able to recover it completely. We focus on PSMT protocols that work in two transmission rounds for n = 2t + 1. We break from previous work by following a conceptually simpler blueprint. This has two consequences: first, we obtain improved efficiency, namely, we reduce the previously best-known communication complexity, i.e. the number of transmitted bits necessary to communicate a 1- bit secret, from O(n3 log n) to O(n2 log n). Our solution also reaches optimal transmission rate for a secret of size O(n log n), thus answering the hitherto open question of attaining a threshold below O(n2 log n) bits. Second, our construction can be adapted to more general scenarios relevant to Network Coding, where the adversary is given more power.

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The road transport sector is currently facing significant challenges, due in part to CO₂ emissions, high fuel prices, and a shortage of staff. These issues are partially caused by more than 40% of truck journeys being “empty runs” in some member states of the European Union and heavy under-utilization of deck space for non-empty runs. In order to overcome said inefficiency, this paper proposes a decentralized platform to facilitate collaborative transport networks (CTNs), i.e., to enable horizontal collaboration to increase load factors and reduce costs and CO₂ emissions. Our solution leverages secure multi-party computation (MPC) to guarantee that no sensitive business information is leaked to competing hauliers. The system optimizes truck assignments by modeling logistics as a weighted graph that considers orders and truck capacities while maintaining strict confidentiality. Our approach addresses key barriers to CTN adoption, such as lack of trust and data privacy. Implemented using MPyC without extensive optimizations, we demonstrate the efficiency and effectiveness in increasing the average load factor, while achieving acceptable running times (in the order of hours) for arguably meaningful instance sizes (up to 1000 orders). After leveraging a rather simplistic modeling inspired by previous work, we finally give an outlook of possible extensions toward more realistic models and estimate their impact on efficiency.