Efficient and privacy-preserving skyline computation framework across domains

Skyline computation, which returns a set of interesting points from a potentially huge data space, has attracted considerable interest in big data era. However, the flourish of skyline computation still faces many challenges including information security and privacy-preserving concerns. In this paper, we propose a new efficient and privacy-preserving skyline computation framework across multiple domains, called EPSC. Within EPSC framework, a skyline result from multiple service providers will be securely computed to provide better services for the client. Meanwhile, minimum privacy disclosure will be elicited from one service provider to another during skyline computation. Specifically, to leverage the service provider's privacy disclosure and achieve almost real-time skyline processing and transmission, we introduce an efficient secure vector comparison protocol (ESVC) to construct EPSC, which is exclusively based on two novel techniques: fast secure permutation protocol (FSPP) and fast secure integer comparison protocol (FSIC). Both protocols allow multiple service providers to calculate skyline result interactively in a privacy-preserving way. Detailed security analysis shows that the proposed EPSC framework can achieve multi-domain skyline computation without leaking sensitive information to each other. In addition, performance evaluations via extensive simulations also demonstrate the EPSC's efficiency in terms of providing skyline computation and transmission while minimizing the privacy disclosure across different domains. We propose an efficient and privacy-preserving skyline computation framework for multi-domains.Lightweight Additive Homomorphic Public Key Encryption Scheme are proposed for big data processing.Fast Secure Permutation Protocol and Fast Secure Integer Comparison Protocol are designed.Skyline set can be computed in an efficient and a privacy-preserving way by extensive simulation.