Purpose: This paper analyses the feasibility of tracking data originality for pharmaceutical manufacturing in a tamper-proof manner using a geographically distributed system. The main research question is whether it is possible to ensure the traceability of drug manufacturing through the use of smart contracts and a private blockchain network. Methods: This work employs a private Ethereum network with a proof-of-authority consensus algorithm to allow participating nodes to commit the medicament manufacturing originality as transactions in blocks. We use smart contracts to assess the “Original” principle of the ALCOA+ data integrity principles for full sensor-enabled production lines within pharmaceutical manufacturing plants. We have evaluated our data originality assessment approach employing a temporal series of 1300 reports generated based on real datasets from pharma production lines. Out of these reports, 300 reports have been randomly tampered with to make them “unoriginal” (i.e., falsified). Results: Evaluation consistently shows that the proposed approach systematically detects all the manufacturing records whether original or not, together with any source of falsification. By randomly injecting four common data falsification types, their approach effectively detects tampering and ensures the authenticity of the data originality acquired by sensors within manufacturing lines. Conclusion: The approach of using a private blockchain network with a proof-of-authority consensus algorithm and smart contracts is a feasible method to track data originality for pharmaceutical manufacturing in a tamper-proof manner. In addition, this approach effectively detects tampering and ensures the authenticity of the data originality acquired by sensors within manufacturing lines.

As a recommendation technique based on historical user information, collaborative filtering typically predicts the classification of items using a single criterion for a given user. However, many application domains can benefit from the analysis of multiple criteria, e.g. tourists usually rate attractions (hotels, attractions, restaurants, etc.) using multiple criteria. In this paper, we argue that the personalised combination of multi-criteria data together with the creation and application of trust models should not only refine the tourist profile, but also improve the quality of the collaborative recommendations. The main contributions of this work are: (1) a novel profiling approach which takes advantage of the multi-criteria crowdsourced data and builds pairwise trust models and (2) the k-NN prediction of user ratings using trust-based neighbour selection. Significant experimental work has been performed using crowdsourced datasets from the Expedia and TripAdvisor platforms.