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, security, and trust issues in smart environments

Recent advances in networking, handheld computing and sensor technologies have driven forward research towards the realisation of Mark Weiser's dream of calm and ubiquitous computing (variously called pervasive computing, ambient computing, active spaces, the disappearing computer or context-aware computing). In turn, this has led to the emergence of smart environments as one significant facet of research in this domain. A smart environment, or space, is a region of the real world that is extensively equipped with sensors, actuators and computing components [1]. In effect the smart space becomes a part of a larger information system: with all actions within the space potentially affecting the underlying computer applications, which may themselves affect the space through the actuators. Such smart environments have tremendous potential within many application areas to improve the utility of a space. Consider the potential offered by a smart environment that prolongs the time an elderly or infirm person can live an independent life or the potential offered by a smart environment that supports vicarious learning

Analytic surveillance: Big data business models in the time of privacy awareness

 Massive data collection and analysis is at the heart of many business models today. New technologies allow for fine-grained recommendation systems that help companies make accurate market predictions while also providing clients with highly personalized services. Because of this, extreme care must be taken when it comes to storing and managing personal (often highly sensitive) information. In this paper we focus on the influence of big data management in media business content platforms, mainly in well-known OTT (Over the Top) services. In addition, we comment on the implications of data management in social networks. We discuss the privacy and security risks associated with this novel scenario, and briefly comment on tools that aid in securing the privacy of business intelligence within this context

BLC: Private Matrix Factorization Recommenders via Automatic Group Learning

We propose a privacy-enhanced matrix factorization recommender that exploits the fact that users can often be grouped together by interest. This allows a form of “hiding in the crowd” privacy. We introduce a novel matrix factorization approach suited to making recommendations in a shared group (or “nym”) setting and the BLC algorithm for carrying out this matrix factorization in a privacy-enhanced manner. We demonstrate that the increased privacy does not come at the cost of reduced recommendation accuracy