Generating Private Recommendation System Using Multiple Homomorphic Encryption Scheme

The recommender system is important tool in online application to generate the recommendation services. Recommendations are generated by collecting the data from users need; online services access the user’s profiles for generating useful recommendations. Privacy sensitive data is used for to collect the data. Collaborative filtering technique gives privacy for sensitive data if data is misused by other service providers or leaked. Existing system uses Paillier encryption algorithm & DGK algorithm to secure user data from malicious third party as well as to protect the private data against service provider but system is more complex and inefficient. Proposed system protects the privacy of user using encrypting the sensitive data. The system uses multiple homomorphic algorithms to secure user data from service providers. The system is used to protect the confidential data of user against the service provider while providing online services. Encrypting private data is recommended and process on data to generate recommendations. To construct efficient system that does not require the active participation of the user. The experiment shows that the result that provide the security by hiding the personal data of user from third party DOI: 10.17762/ijritcc2321-8169.15076

Survey Paper on Generating Customer Relationship Management Efficiently using Homomorphic Encryption and Data Packing

In recommender systems, recommendations are generated based on the data collected from the user. The important requirement of the basic Information Filtering architectures is to protect the privacy of all the users. By using the Homomorphic encryption and data packing the recommender system provides good privacy of customer data. The data protection system gives security from malicious third parties, but does not provide security from the service provider. In this paper, our aim is to generate the dynamic recommendations and protect the confidential data of user against the service provider while protecting the functionality of the system. This system is very useful to generate dynamic recommendations by preserving the privacy of the users

Privacy enhanced recommender system

Recommender systems are widely used in online applications since they enable personalized service to the users. The underlying collaborative filtering techniques work on user’s data which are mostly privacy sensitive and can be misused by the service provider. To protect the privacy of the users, we propose to encrypt the privacy sensitive data and generate recommendations by processing them under encryption. With this approach, the service provider learns no information on any user’s preferences or the recommendations made. The proposed method is based on homomorphic encryption schemes and secure multiparty computation (MPC) techniques. The overhead of working in the encrypted domain is minimized by packing data as shown in the complexity analysis

On content-based recommendation and user privacy in social-tagging systems

Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft