Generating Private Recommendations Using ElGamal Homomorphic Encryption (International Journal of Engineering and Computer Science, 2015)
In E-commerce, Recommender systems have become an important tool since it is used to personalize the online services and products. Users are concerned about the privacy as personal information can be misused easily. Privacy-sensitive data plays an important role in generating recommendations in online services. Conventional data protection mechanisms provide security only against malicious third parties, as their focus is on access control and secure transmission, but it does not secure by service provider. This creates a major privacy risk for the users. In this paper, we aim to protect the private data from the service provider with functionality of the system is preserved; Private data is encrypted and processed under encryption to generate recommendations. This mechanism becomes highly efficient system by introducing a semi trusted third party, so that system does not require the active participation of user. We have proposed a homomorphic encryption using ElGamal cryptosystem as it is faster, requires considerable less time to decrypt and easier to do distributed key generation. We also aim one comparison protocol, for the comparison of multiple values that are packed in one encryption. According to this work, system can generate private recommendations in privacy preserving manner.