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Developments in the field of genomic studies have resulted in the current high availability of genomic data which, in turn, raises significant privacy concerns. As DNA information is unique and correlated among family members, it cannot be regarded just as a matter of individual privacy concern. Due to the need for privacyenhancing methods to protect these sensitive pieces of information, cryptographic solutions are deployed and enabled scientists to work on encrypted genomic data. In this paper, we develop an attributebased privacy-preserving susceptibility testing method in which genomic data of patients is outsourced to an untrustworthy platform. We determine the challenges for the computations required to process the outsourced data and access control simultaneously within patient-doctor interactions. We obtain a non-interactive scheme regarding the contribution of the patient which improves the safety of the user data. Moreover, we exceed the computation performance of the susceptibility testing over the encrypted genomic data while we manage attributes and embedded access policies. Also, we guarantee to protect the privacy of individuals in our proposed scheme.

Consider two data holders, ABC and XYZ, with graph data (e.g., social networks, e-commerce, telecommunication, and bio-informatics). ABC can see that node A is linked to node B, and XYZ can see node B is linked to node C. Node B is the common neighbour of A and C but neither network can discover this fact on their own. In this paper, we provide a two party computation that ABC and XYZ can run to discover the common neighbours in the union of their graph data, however neither party has to reveal their plaintext graph to the other. Based on private set intersection, we implement our solution, provide measurements, and quantify partial leaks of privacy. We also propose a heavyweight solution that leaks zero information based on additively homomorphic encryption.

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