Economic location-based services, privacy and the relationship to identity

Mobile telephony and mobile internet are driving a new application paradigm: location-based services (LBS). Based on a person’s location and context, personalized applications can be deployed. Thus, internet-based systems will continuously collect and process the location in relationship to a personal context of an identified customer. One of the challenges in designing LBS infrastructures is the concurrent design for economic infrastructures and the preservation of privacy of the subjects whose location is tracked. This presentation will explain typical LBS scenarios, the resulting new privacy challenges and user requirements and raises economic questions about privacy-design. The topics will be connected to “mobile identity” to derive what particular identity management issues can be found in LBS

An OAuth2-based protocol with strong user privacy preservation for smart city mobile e-Health apps

In the context of the Smart City concept, mobile e-Health applications can play a pivotal role towards the improvement of citizens’ quality of life, since they can enable citizens to access personalized e-Health services, without limitations on time and location. However, accessing personalized e-Health services through citizens’ mobile e-Health applications, running on their mobile devices, raises many privacy issues in terms of citizens’ identity and location. These privacy issues should be addressed so that citizens, concerned about privacy leakage, will embrace Smart City mobile e-Health applications and reap their benefits. Hence, in this paper we propose an OAuth2-based protocol with strong user privacy preservation that addresses these privacy issues. Our proposed protocol follows the OAuth2 protocol flow and integrates a pseudonym-based signature scheme and a delegation signature scheme into the user authentication phase of the OAuth2 protocol. The proposed protocol enables citizens authentication towards the servers providing personalized e-Health services, while preserving their privacy from malicious mobile applications and/or eavesdroppers. Moreover, the proposed protocol does not require to store sensitive information in the citizens’ mobile devices

Privacy-Preserved and Best-effort Provisions of Cyber-I Information to Personalized Services

Abstract—User information is needed for personalized services. However, personalized services are often confused either by information inconsistency and incorrectness or the cold start issue while they gather user information. Even some services, such as social network services, provide user information for personalized services to overcome these problems, they are also facing difficulty of the limited diversity of user information. Additionally, they are only capable of providing existing information. While a Cyber-I (short for Cyber Individual) collects any information of a person in its way to gradually approximate to its user. Certainly, a user information needed by personalized services is also included in a corresponding Cyber-I. Therefore, providing Cyber-I information to personalized services could be more prospecting. In order to provide Cyber-I information to personalized services, there are two main problems should be solved. One is the privacy protection problem. Methods should be designed to provide privacy preservation for user. Another one is the best-effort issue which is about how to make full use of existing Cyber-I information to satisfy personalized services as much as possible. Thus, the goal of this paper is providing Cyber-I information to personalized services by best-efforts provisions, simultaneously provide privacy preservation for Cyber-I. To reach that goal, a Cyber-I Information Provision System (CIPS) is proposed. Keywords—Cyber-I; Real-I; personalized service; privacy preservation; information provisio

Survey on Privacy Preservation in Personalized Web Environment

Personalized web search (PWS) is a general category of search techniques aiming at providing different search results for different users or organize search results differently for each user, based upon their interest, preferences and information needs. As the expense, user information has to be collected and analyzed to figure out the user intention behind the issued query. However, users are uncomfortable with exposing private information during search which has become a major barrier for the wide proliferation of PWS. Search engines should provide security mechanism such that user will be ensured of its privacy and its information should be kept safe. Many personalization techniques are giving access to achieve personalization of user’s web search. Search engines can provide more accurate and specific data if users trust search engine and provide more information. But users should be ensured that their private information should be kept safe. In this paper we will discuss on different techniques on personalized web search and securing personalized information. DOI: 10.17762/ijritcc2321-8169.16041

Survey on privacy preservation in personalized web environment

Personalized web search (PWS) is a general category of search techniques aiming at providing different search results for different users or organize search results differently for each user, based upon their interest, preferences and information needs. As the expense, user information has to be collected and analyzed to figure out the user intention behind the issued query. However, users are uncomfortable with exposing private information during search which has become a major barrier for the wide proliferation of PWS. Search engines should provide security mechanism such that user will be ensured of its privacy and its information should be kept safe. Many personalization techniques are giving access to achieve personalization of user’s web search. Search engines can provide more accurate and specific data if users trust search engine and provide more information. But users should be ensured that their private information should be kept safe. In this paper we will discuss on different techniques on personalized web search and securing personalized information. DOI: 10.17762/ijritcc2321-8169.16040

PERSONALIZED POINT OF INTEREST RECOMMENDATIONS WITH PRIVACY-PRESERVING TECHNIQUES

Location-based services (LBS) have become increasingly popular, with millions of people using mobile devices to access information about nearby points of interest (POIs). Personalized POI recommender systems have been developed to assist users in discovering and navigating these POIs. However, these systems typically require large amounts of user data, including location history and preferences, to provide personalized recommendations. The collection and use of such data can pose significant privacy concerns. This dissertation proposes a privacy-preserving approach to POI recommendations that address these privacy concerns. The proposed approach uses clustering, tabular generative adversarial networks, and differential privacy to generate synthetic user data, allowing for personalized recommendations without revealing individual user data. Specifically, the approach clusters users based on their fuzzy locations, generates synthetic user data using a tabular generative adversarial network and perturbs user data with differential privacy before it is used for recommendation. The proposed approaches achieve well-balanced trade-offs between accuracy and privacy preservation and can be applied to different recommender systems. The approach is evaluated through extensive experiments on real-world POI datasets, demonstrating that it is effective in providing personalized recommendations while preserving user privacy. The results show that the proposed approach achieves comparable accuracy to traditional POI recommender systems that do not consider privacy while providing significant privacy guarantees for users. The research\u27s contribution is twofold: it compares different methods for synthesizing user data specifically for POI recommender systems and offers a general privacy-preserving framework for different recommender systems. The proposed approach provides a novel solution to the privacy concerns of POI recommender systems, contributes to the development of more trustworthy and user-friendly LBS applications, and can enhance the trust of users in these systems

User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy

Recommender systems have become an integral part of many social networks and extract knowledge from a user's personal and sensitive data both explicitly, with the user's knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used. In this context, several works have been done to address privacy concerns for usage in online social network data and by recommender systems. This paper surveys the main privacy concerns, measurements and privacy-preserving techniques used in large-scale online social networks and recommender systems. It is based on historical works on security, privacy-preserving, statistical modeling, and datasets to provide an overview of the technical difficulties and problems associated with privacy preserving in online social networks.Comment: 26 pages, IET book chapter on big data recommender system