Privacy-Preserving Outsource Computing for Binary Vector Similarity

  • Dan Yang
  • Yu-Chi Chen
  • Shaozhen Ye
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 733)


The preservation of privacy has become a widely discussed topic on the Internet. Encryption is an approach to privacy; however, to outsource computing to an cloud service without revealing private information over encrypted data is difficult. Homomorphic encryption can contribute to it but is based on complicated mathematical structures of abstract algebra. We propoase a new scheme for securely computing the similarity between binary vectors through a cloud server. The scheme is constructed from ciphertext policy attribute based encryption and garbled circuits rather than homomorphic encryption. Attribute based encryption provides the access power, which is a necessary primitive in our scheme. Moreover, for computing over encrypted data, we rely on garbled circuits to handle secure outsourcing and to avoid the use of homomorphic encryption.


Privacy preservation Outsourced computing Data search Garbled circuit 



This work was partially supported by the Innovation Center for Big Data and Digital Convergence, Yuan Ze University, and the Ministry of Science and Technology of Taiwan under grant no. 106-2218-E-155-008-MY3. We also acknowledge Wallace Academic Editing for editing this manuscript.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Mathematics and Computer ScienceFuzhou UniversityFuzhouPeople’s Republic of China
  2. 2.Department of Computer Science and EngineeringYuan Ze UniversityTaoyuanTaiwan

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