An anonymization-based privacy-preserving data collection protocol for digital health data - Andrew, J.; Eunice, R. Jennifer; Karthikeyan, J. (Frontiers in Public Health, 2023)
Digital health data collection is vital for healthcare and medical research. But it contains sensitive information about patients which makes it challenging. To collect health data without privacy breaches it must be secured between the data owner and collector. Existing data collection research studies have too stringent assumptions such as using a third-party anonymizer or a private channel amid the data owner and the collector. These studies are more susceptible to privacy attacks due to third parties involvement. This makes them less applicable for privacy-preserving healthcare data collection. This paper proposes a novel privacy-preserving data collection protocol that anonymizes healthcare data without using a third-party anonymizer or a private channel for data transmission. A clustering-based k-anonymity model is adopted to efficiently prevent identity disclosure attacks and the communication between the data owner and the collector is restricted to some elected representatives of each equivalent group of data owners. We also identified a privacy attack known as “Leader Collusion”, in which the elected representatives may collaborate to violate an individual’s privacy. We propose solutions for such collisions and sensitive attribute protection. A greedy heuristic method is devised to efficiently handle the data owners who join or depart the anonymization process dynamically. Furthermore, we present the potential privacy attacks on the proposed protocol and theoretical analysis. Extensive experiments are conducted on real-world datasets and the results suggest that our solution outperforms the state-of-the-art techniques in terms of privacy protection and computational complexity.