Coupling learning is designed to estimate, discover and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand-it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this work, we develop a distributed collaborative coupling learning system which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favourable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs. Index Terms-Coupling learning, differential privacy, collaborative learning COUPLING LEARNING is an emerging research topic that refers to understanding, formalizing and quantifying the complex relations and interactions, i.e., couplings hidden in complex data. Effective discovery and extraction of the

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Genome-wide analysis has demonstrated both health and social benefits. However, large scale sharing of such data may reveal sensitive information about individuals. One of the emerging challenges is identity tracing attack that exploits correlations among genomic data to reveal the identity of DNA samples. In this paper, we first demonstrate that the adversary can narrow down the sample's identity by detecting his/her genetic relatives and quantify such privacy threat by employing a Shannon entropy-based measurement. For example, we exemplify that when the dataset size reaches 30% of the population, for any target from that population, the uncertainty of the target's identity is reduced to merely 2.3 bits of entropy (i.e., the identity is pinned down within 5 people). Direct application of existing approaches such as differential privacy (DP), secure multiparty computation (MPC) and homomorphic encryption (HE) may not be applicable to this challenge in genome-wide analysis because of the compromise on utility (i.e., accuracy or efficiency). Towards addressing this challenge, this paper proposes a framework named υFRAG to facilitate privacy-preserving data sharing and computation in genome-wide analysis. υFRAG mitigates privacy risks by using a vertical fragmentation to disrupt the genetic architecture on which the adversary relies for identity tracing without sacrificing the capability of genome-wide analysis. We theoretically prove that it preserves the correctness of the primitive functionalities and algorithms ranging from basic summary statistics to advanced neural networks. Our experiments demonstrate that υFRAG outperforms secure multiparty computation (MPC) and homomorphic encryption (HE) protocols, with a speedup of more than 221x for training neural networks, and also traditional non-private algorithms and a state-of-the-art noise-based differential privacy (DP) solution in most settings.

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