A hybrid strategy for privacy-preserving recommendations for mobile shopping

To calculate recommendations, recommender systems col-lect and store huge amounts of users ’ personal data such as preferences, interaction behavior, or demographic infor-mation. If these data are used for other purposes or get into the wrong hands, the privacy of the users can be com-promised. Thus, service providers are confronted with the challenge of o↵ering accurate recommendations without the risk of dissemination of sensitive information. This paper presents a hybrid strategy combining collaborative filtering and content-based techniques for mobile shopping with the primary aim of preserving the customer’s privacy. Detailed information about the customer, such as the shopping his-tory, is securely stored on the customer’s smartphone and locally processed by a content-based recommender. Data of individual shopping sessions, which are sent to the store backend for product association and comparison with simi-lar customers, are unlinkable and anonymous. No uniquely identifying information of the customer is revealed, making it impossible to associate successive shopping sessions at the store backend. Optionally, the customer can disclose demo-graphic data and a rudimentary explicit profile for further personalization

A heuristic tool for exposure reduction in indoor wireless networks

A heuristic indoor network planner for exposure calculation and optimization in wireless networks is developed. The model for the electric-field strength in the vicinity of an access point is presented and the WiFi networks are optimized in order not to exceed a maximal electric-field strength at a certain separation from the access points. The influence of the maximally allowed field strength and the assumed minimal separation between the access point and the human is assessed for a typical office building

Comparison of uplink SAR values in train environment for different wireless technologies

A comparison of uplink Specific Absorption Rate (SAR) values in a train environment for different wireless technologies using macrocells or femtocells is presented. The comparison is based on both simulations and real-life experiments. Compared to a typical GSM900 macrocell scenario, the largest SAR reduction is observed when using a UMTS femtocell base station, which leads to uplink SAR reductions by a factor 358,820

Coverage prediction and optimization algorithms for indoor environments

A heuristic algorithm is developed for the prediction of indoor coverage. Measurements on one floor of an office building are performed to investigate propagation characteristics and validations with very limited additional tuning are performed on another floor of the same building and in three other buildings. The prediction method relies on the free-space loss model for every environment, this way intending to reduce the dependency of the model on the environment upon which the model is based, as is the case with many other models. The applicability of the algorithm to a wireless testbed network with fixed WiFi 802.11b/g nodes is discussed based on a site survey. The prediction algorithm can easily be implemented in network planning algorithms, as will be illustrated with a network reduction and a network optimization algorithm. We aim to provide an physically intuitive, yet accurate prediction of the path loss for different building types