Biblio
With the popularity of cloud computing, database outsourcing has been adopted by many companies. However, database owners may not 100% trust their database service providers. As a result, database privacy becomes a key issue for protecting data from the database service providers. Many researches have been conducted to address this issue, but few of them considered the simultaneous transparent support of existing DBMSs (Database Management Systems), applications and RADTs (Rapid Application Development Tools). A transparent framework based on accessing bridge and mobile app for protecting database privacy with PKI (Public Key Infrastructure) is, therefore, proposed to fill the blank. The framework uses PKI as its security base and encrypts sensitive data with data owners' public keys to protect data privacy. Mobile app is used to control private key and decrypt data, so that accessing sensitive data is completely controlled by data owners in a secure and independent channel. Accessing bridge utilizes database accessing middleware standard to transparently support existing DBMSs, applications and RADTs. This paper presents the framework, analyzes its transparency and security, and evaluates its performance via experiments.
Many applications of mobile computing require the computation of dot-product of two vectors. For examples, the dot-product of an individual's genome data and the gene biomarkers of a health center can help detect diseases in m-Health, and that of the interests of two persons can facilitate friend discovery in mobile social networks. Nevertheless, exposing the inputs of dot-product computation discloses sensitive information about the two participants, leading to severe privacy violations. In this paper, we tackle the problem of privacy-preserving dot-product computation targeting mobile computing applications in which secure channels are hardly established, and the computational efficiency is highly desirable. We first propose two basic schemes and then present the corresponding advanced versions to improve efficiency and enhance privacy-protection strength. Furthermore, we theoretically prove that our proposed schemes can simultaneously achieve privacy-preservation, non-repudiation, and accountability. Our numerical results verify the performance of the proposed schemes in terms of communication and computational overheads.