Share your data carefree: An efficient, scalable and privacy-preserving data sharing service in cloud computing

Benefiting from the powerful computing and storage capabilities of cloud services, data sharing in the cloud has been permeated across various applications including social networks, e-health and crowdsourcing transportation system. Intuitively, outsourcing data to untrusted cloud commonly raises concerns about data privacy breaches. To combat this, one approach is exploiting Broadcast Based Searchable Encryption (BBSE) for secure data sharing. Nevertheless, the latest proposed BBSE is still defective in either security or efficiency. In this paper, we propose ESPD, an Efficient, Scalable and Privacy-preserving Data sharing framework over encrypted cloud dataset. Different from previous works, ESPD supports sharing target data to multiple users with distinct secret keys, and keeps a constant ciphertext length with the changes of the amount of system users. This feature significantly improves search efficiency and makes ESPD scalable in real-world scenarios. We show a formal analysis to prove the security of ESPD in terms of file privacy, keyword privacy and trapdoor privacy. Also, extensive experiments on real-world dataset are conducted to indicate the desirable performance of ESPD compared to other similar schemes.