Articles in Press

To enable data analytics that provides valuable insights, data that are distributed across several organisations increasingly need to be shared before they can be analysed. However, sharing data from different sources can raise privacy and confidentiality concerns. Organisations are often unwilling or not allowed to share their sensitive data, such as personal details or health or financial data, with other parties because this potentially violates the privacy of individuals. Secure multi-party computation (SMC) has been introduced as a solution to overcome the problem of performing computations on sensitive data across organisations. SMC allows parties to jointly compute a function over their inputs while preserving the privacy of these inputs. Secure summation protocols are an important building block in many SMC applications that can be used under two different SMC models (i.e. with and without the involvement of a third party to conduct the computations). A secure summation protocol is used to compute the summation of private inputs held by different parties. In this paper we study existing secure summation protocols that can be used under different SMC models and then propose three advanced secure summation protocols that use homomorphic encryption. We then consider different scenarios of how parties might collude with each other in secure summation protocols, and the potential collusion risks that occur with these protocols. No such investigation of possible collusion scenarios for secure summation protocols has so far been presented. We analyse each secure summation protocol under different collusion scenarios and evaluate the efficiency of each protocol with different numbers of parties and different input data sizes. Our evaluation shows that our proposed protocols provide improved privacy against collusion risks and they can calculate a sum more efficiently compared to existing secure summation protocols.

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