Nowadays, the management and analyses of ‘big data’ are becoming indispensable for numerous organizations all over the world. In many cases, multiple organizations want to perform data analyses on their combined databases. Skyline query is one of the popular operations for selecting representative objects from a large database, where any other object within the database does not dominate each of the representative objects, called ‘skyline’. Like other data analytics operations, the multi-party skyline query can provide benefits to the participating organizations by retrieving the skyline objects from their combined databases. Such a multi-party skyline query demands the disclosure of individual parties’ objects to others during the computation. But, owing to the data privacy and security concern of the present IT era, such disclosure of the individual parties’ databases is strictly prohibited. Considering this issue, we are proposing a new framework for the privacy-preserving multi-party skyline query, exploiting additive homomorphic encryption along with data anonymization, perturbation, and randomization techniques. The underlying protocols within our proposed framework ensure that every participating party can identify its multi-party skyline objects without revealing the objects to others during the multi-party skyline query. The detailed privacy and security analyses show that the proposed framework can achieve the desired computation goal without privacy leakage. Besides, the performance evaluation through complexity analyses, extensive simulations, and comprehensive comparison also demonstrate the utility and the efficiency of the proposed framework.

Introduction

Organizations throughout the world are producing a vast amount of data, known as ‘big data’. Consequently, the demand for big data analytics tools is growing rapidly. These tools have attracted massive attention to organizations and researchers for making strategic decisions and for new knowledge acquisitions. Open market product pricing, risk management in investment, consumer buying pattern analysis, financial transaction analysis, health data analysis, etc. are remarkable examples of big data analyses. Still, big data is introducing new challenges for collection, storage, process, analyze, etc. In the current trend of IT, multiple organizations dealing with similar kind of services are collecting compatible big data, and have noticed the importance of analytical results that can be found from the union of their databases. Such sort of joint data analyses requires multi-party computation over the combined databases of all organizations. Since many organizational databases may contain various sensitive data like personal or financial data, revealing these data can seriously violate the individuals’ privacy and can be the reason of significant financial and goodwill loss for the organizations.