Outsourced privacy-aware task allocation with flexible expressions in crowdsourcing

Abstract In crowdsourcing, task allocation service helps to screen out suitable workers to realize more extensive perceptual data collection. However, due to the vulnerability of existing technologies, it is also subject to security and privacy issues. Although there exist some related works that enable to protect the privacy of both requesters and workers, they cannot well support flexible and expressive matching polices for multi-keyword in multi-worker scenarios. Since the inner-product encryption (IPE) mechanism enables to support multiple types of constraint expressions, a construction of privacy-aware task allocation system based on two forms of IPE is proposed in this paper. In such a scheme, only the crowdsourcing service provider (CSP) has the ability to perform fine-grained inner-product policy matching over the encrypted position and other attributes. Moreover, the CSP could calculate the corresponding conversion ciphertext for workers who meet the conditions, thus reducing the amount of user calculation. And meanwhile, through the security proof, we prove the indistinguishability for task content and location information, respectively. Furthermore, the implementation analysis and evaluation show that the scheme is feasible and effective.