Privacy-preserving Distributed Data Mining: Techniques, Challenges, Applications and Future Research Directions
Sangeeta Borkakoty *
Department of Computer Science, University of Science and Technology, Meghalaya, India.
Kanak Chandra Bora
Department of Computer Science, University of Science and Technology, Meghalaya, India.
*Author to whom correspondence should be addressed.
Abstract
Distributed data mining has become central to extracting knowledge from data held by institutions, devices and organisations that cannot legitimately or safely pool their records. Yet keeping raw data at source does not, by itself, ensure privacy: intermediate statistics, gradients, identifiers, query patterns and final models can disclose information, while controls that prevent disclosure may weaken analytical validity or the detection of malicious participants. This critical narrative review evaluates the development of privacy-preserving distributed data mining from partition-aware secure computation to federated learning, differential privacy, protected record linkage and hybrid architectures. Scholarly literature was selected through topic-specific searches of accessible bibliographic resources, reference tracing and verification against publication and digital-object-identifier records, with an emphasis on peer-reviewed methodological, empirical and review evidence. The synthesis distinguishes cryptographic input confidentiality, statistical protection of released outputs and operational governance, showing why these objectives cannot be treated as interchangeable. Evidence supports the feasibility of selected secure aggregation, multiparty computation and cross-institutional modelling workflows, but performance and privacy claims depend materially on adversarial assumptions, participant numbers, data topology, privacy accounting, model access and deployment conditions. Demonstrations of gradient reconstruction and membership inference establish credible attack surfaces, whereas more recent experimental evidence shows that exploitability is conditional rather than uniform. Healthcare collaborations provide instructive feasibility evidence, but do not establish universal effectiveness, regulatory compliance or immunity from model leakage. Five evidence-mapping tables integrate architectural distinctions, protective mechanisms, adversarial findings, application maturity and research priorities. The review argues for evaluation based on the entire information-release lifecycle, joint testing of confidentiality, integrity and utility, and transparent reporting of threats, costs and residual risks. Future progress depends less on promoting a single technique than on auditable combinations whose guarantees remain meaningful under heterogeneous data, collusion, repeated analysis and institutional constraints.
Keywords: Secure multiparty computation, differential privacy, federated learning, secure aggregation, private set intersection, gradient inversion, data heterogeneity, privacy–utility trade-off