Data Privacy Preservation in Collaborative Filtering Based Recommender Systems

This dissertation studies data privacy preservation in collaborative filtering based recommender systems and proposes several collaborative filtering models that aim at preserving user privacy from different perspectives. The empirical study on multiple classical recommendation algorithms presents the basic idea of the models and explores their performance on real world datasets. The algorithms that are investigated in this study include a popularity based model, an item similarity based model, a singular value decomposition based model, and a bipartite graph model. Top-N recommendations are evaluated to examine the prediction accuracy. It is apparent that with more customers\u27 preference data, recommender systems can better profile customers\u27 shopping patterns which in turn produces product recommendations with higher accuracy. The precautions should be taken to address the privacy issues that arise during data sharing between two vendors. Study shows that matrix factorization techniques are ideal choices for data privacy preservation by their nature. In this dissertation, singular value decomposition (SVD) and nonnegative matrix factorization (NMF) are adopted as the fundamental techniques for collaborative filtering to make privacy-preserving recommendations. The proposed SVD based model utilizes missing value imputation, randomization technique, and the truncated SVD to perturb the raw rating data. The NMF based models, namely iAux-NMF and iCluster-NMF, take into account the auxiliary information of users and items to help missing value imputation and privacy preservation. Additionally, these models support efficient incremental data update as well. A good number of online vendors allow people to leave their feedback on products. It is considered as users\u27 public preferences. However, due to the connections between users\u27 public and private preferences, if a recommender system fails to distinguish real customers from attackers, the private preferences of real customers can be exposed. This dissertation addresses an attack model in which an attacker holds real customers\u27 partial ratings and tries to obtain their private preferences by cheating recommender systems. To resolve this problem, trustworthiness information is incorporated into NMF based collaborative filtering techniques to detect the attackers and make reasonably different recommendations to the normal users and the attackers. By doing so, users\u27 private preferences can be effectively protected

A Cascade Framework for Privacy-Preserving Point-of-Interest Recommender System

Point-of-interest (POI) recommender systems (RSes) have gained significant popularity in recent years due to the prosperity of location-based social networks (LBSN). However, in the interest of personalization services, various sensitive contextual information is collected, causing potential privacy concerns. This paper proposes a cascaded privacy-preserving POI recommendation (CRS) framework that protects contextual information such as user comments and locations. We demonstrate a minimized trade-off between the privacy-preserving feature and prediction accuracy by applying a semi-decentralized model to real-world datasets

Guiding Us Throughout a Sea of Data - A Survey on Recommender Systems and Its Privacy Challenges

Over the past decades, the Internet has served as the backbone connecting people to others, places and things. With the sheer volume of information generated everyday, people can feel overwhelmed when having to make a selection among the multiple options that normally come up after a search or application request. For example, when searching for news articles regarding a particular topic, the search engine will present a number of results to you. When looking for some product on shopping websites, there are usually several pages of results that match the keywords. It can be very challenging for people to find their most expected information in the era of big data. A recommender system is a program that utilizes algorithms to learn users’ preferences from historical data, and predict their future interests. Recommender systems are employed everywhere in the cyberspace. Many websites including Amazon, eBay, YouTube, Facebook, Netflix, and others, have integrated automatic personalized recommendation techniques into their systems, in order to help users find their most desired information. While recommender systems have become a common feature on most web applications and sites, one of the major issues around its use is privacy concerns. A regular recommender system requires the users to share their online behavior data, such as their past shopping records, browsing history, visited places, so that it can learn their preferences. This can potentially deter people from using the system because these data are considered as users’ privacy and many do not feel comfortable sharing the information with other parties. In this research, we studied several recommendation algorithms, and compared their performance as well as prediction accuracy on real-world datasets. We also proposed a novel nonnegative matrix factorization (NMF) based privacy-preserving point-of-interest recommendation framework, in which the latent factors in NMF are learned on user group preference instead of individual user preference. Recommendations are made by personalizing the group preference on user’s local devices. There are no location or check-in data collected from the users anywhere throughout the learning and recommendation processes. Some preliminary results on a regular recommender system were established and two GUI applications were developed. The on-going research focuses on integrating the privacy-preserving framework into the system and verifying the effectiveness as well as the recommendation accuracy of the proposed model

Building Extraction from LiDAR Point Clouds Based on Revised RandLA-Net

3D building models is crucial for applications in smart cities. Automatic reconstruction of 3D buildings has been investigated based on various data sources. Point clouds from airborne LiDAR scanners can be used to extract buildings data due to its high accuracy and point density. In this paper, we present a methodology to segment buildings and corresponding rooftop structure from point clouds. First, RandLA-Net, which is an efficient and lightweight neural network for semantic segmentation of large-scale point clouds, is revised and adopted for building segmentation. By implementing local feature aggregation of each point, RandLA-Net can effectively preserve geometric details in point clouds. Besides 3D coordinates of point clouds, we incorporated point attributes including pulse intensity and return numbers into the network as additional features. Feature normalizations are applied to the input features. To achieve a better result of the local feature aggregation, hyperparameters of the network are fine-tuned according to the density of points and building size. Based on the classified building point clouds, DBSCAN clustering algorithm is implemented for segmenting individual buildings. Elevation histogram analysis is conducted to determine optimal threshold values for delineating candidate rooftop point clouds of individual buildings. For the buildings with multiple rooftops, multiple elevation threshold values are necessary to extract corresponding rooftops or walls. Then DBSCAN is employed again for segmentation of individual rooftops and denoising of point clouds of each building. Finally, Alpha-shape analysis is applied based on adaptive threshold values to build the envelope of each rooftop. Experiments show that our implementation of building segmentation using RandLA-net achieves higher mean IoU (Intersection over Union) and better classification performance in building segmentation. ISPRS benchmark data was used in our experiment and our methodology produce results with accuracy of 90.79%

RFID Application of Smart Grid for Asset Management

RFID technology research has resolved practical application issues of the power industry such as assets management, working environment control, and vehicle networking. Also it provides technical reserves for the convergence of ERP and CPS. With the development of RFID and location-based services technology, RFID is converging with a variety of sensing, communication, and information technologies. Indoor positioning applications are under rapid development. Micromanagement environment of the assets is a useful practice for the RFID and positioning. In this paper, the model for RFID applications has been analyzed in the microenvironment management of the data center and electric vehicle batteries, and the optimization scheme of enterprise asset management is also proposed

Is f1(1420)f_1(1420) the partner of f1(1285)f_1(1285) in the 3P1^3P_1 qqˉq\bar{q} nonet?

Based on a $2\times 2$ mass matrix, the mixing angle of the axial vector states $f_1(1420)$ and $f_1(1285)$ is determined to be $51.5^{\circ}$, and the theoretical results about the decay and production of the two states are presented. The theoretical results are in good agreement with the present experimental results, which suggests that $f_1(1420)$ can be assigned as the partner of $f_1(1285)$ in the $^3P_1$ $q\bar{q}$ nonet. We also suggest that the existence of $f_1(1510)$ needs further experimental confirmation.Comment: Latex, 6 pages, to be published in Chin. Phys. let