scholarly journals Privacy-Aware Multidimensional Mobile Service Quality Prediction and Recommendation in Distributed Fog Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Wenwen Gong ◽  
Lianyong Qi ◽  
Yanwei Xu
Keyword(s):  
Service Selection ◽  
Locality Sensitive Hashing ◽  
Mobile Service ◽  
Service Recommendation ◽  
User Privacy ◽  
Service Usage ◽  
Selection Decisions ◽  
Wide Range ◽  
Cross Platform ◽  
Computational Resources

With the ever-increasing popularity of mobile computing technology, a wide range of computational resources or services (e.g., movies, food, and places of interest) are migrating to the mobile infrastructure or devices (e.g., mobile phones, PDA, and smart watches), imposing heavy burdens on the service selection decisions of users. In this situation, service recommendation has become one of the promising ways to alleviate such burdens. In general, the service usage data used to make service recommendation are produced by various mobile devices and collected by distributed edge platforms, which leads to potential leakage of user privacy during the subsequent cross-platform data collaboration and service recommendation process. Locality-Sensitive Hashing (LSH) technique has recently been introduced to realize the privacy-preserving distributed service recommendation. However, existing LSH-based recommendation approaches often consider only one quality dimension of services, without considering the multidimensional recommendation scenarios that are more complex but more common. In view of this drawback, we improve the traditional LSH and put forward a novel LSH-based service recommendation approach named SerRecmulti-qos, to protect users’ privacy over multiple quality dimensions during the distributed mobile service recommendation process.

scholarly journals Robust and Privacy-Preserving Service Recommendation over Sparse Data in Education

2019 ◽  
Vol 2019 ◽  
pp. 1-13
Author(s):  
Xuening Chen ◽  
Hanwen Liu ◽  
Yanwei Xu ◽  
Chao Yan
Keyword(s):  
Service Quality ◽  
Privacy Preservation ◽  
False Negative ◽  
Locality Sensitive Hashing ◽  
Quality Data ◽  
Service Recommendation ◽  
User Privacy ◽  
Target User ◽  
Cross Platform ◽  
Hybrid Collaborative Filtering

Service recommendation has become one of the most effective approaches to quickly extract insightful information from big educational data. However, the sparsity of educational service quality data (from multiple platforms or parties) used to make service recommendations often leads to few even null recommended results. Moreover, to protect sensitive business information and obey laws, preserving user privacy during the abovementioned multisource data integration process is a very important but challenging requirement. Considering the above challenges, this paper integrates Locality-Sensitive Hashing (LSH) with hybrid Collaborative Filtering (HCF) techniques for robust and privacy-aware data sharing between different platforms involved in the cross-platform service recommendation process. Furthermore, to minimize the “False negative” recommended results incurred by LSH and enhance the success of recommended results, we propose two optimization strategies to reduce the probability that similar neighbours of a target user or similar services of a target service are overlooked by mistake. Finally, we conduct a set of experiments based on a real distributed service quality dataset, i.e., WS-DREAM, to validate the feasibility and advantages of our proposed recommendation approach. The extensive experimental results show that our proposal performs better than three competitive methods in terms of efficiency, accuracy, and successful rate while guaranteeing privacy-preservation.

scholarly journals Privacy-Preserving and Scalable Service Recommendation Based on SimHash in a Distributed Cloud Environment

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yanwei Xu ◽  
Lianyong Qi ◽  
Wanchun Dou ◽  
Jiguo Yu
Keyword(s):  
Privacy Preservation ◽  
Service Selection ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Cloud Environment ◽  
Cloud Platform ◽  
Service Recommendation ◽  
Service Usage ◽  
Target User ◽  
Usage Data

With the increasing volume of web services in the cloud environment, Collaborative Filtering- (CF-) based service recommendation has become one of the most effective techniques to alleviate the heavy burden on the service selection decisions of a target user. However, the service recommendation bases, that is, historical service usage data, are often distributed in different cloud platforms. Two challenges are present in such a cross-cloud service recommendation scenario. First, a cloud platform is often not willing to share its data to other cloud platforms due to privacy concerns, which decreases the feasibility of cross-cloud service recommendation severely. Second, the historical service usage data recorded in each cloud platform may update over time, which reduces the recommendation scalability significantly. In view of these two challenges, a novel privacy-preserving and scalable service recommendation approach based on SimHash, named SerRecSimHash, is proposed in this paper. Finally, through a set of experiments deployed on a real distributed service quality dataset WS-DREAM, we validate the feasibility of our proposal in terms of recommendation accuracy and efficiency while guaranteeing privacy-preservation.

scholarly journals Time-Aware Cross-Platform IoT Service Recommendation with Privacy Preservation

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Can Zhang ◽  
Junhua Wu ◽  
Chao Yan ◽  
Guangshun Li
Keyword(s):  
Real World ◽  
Data Privacy ◽  
Privacy Preservation ◽  
Locality Sensitive Hashing ◽  
Service Recommendation ◽  
Cross Platform ◽  
Time Aware ◽  
Temporal Feature

IoT service recommendation techniques can help a user select appropriate IoT services efficiently. Aiming at improving the recommendation efficiency and preserving the data privacy, the locality-sensitive hashing (LSH) technique is adopted in service recommendation. However, existing LSH-based service recommendation methods ignore the intrinsic temporal feature of IoT services. In light of this challenge, we integrate the temporal feature into the conventional LSH-based method and present a time-aware approach with the capability of privacy preservation for IoT service recommendation across multiple platforms. Experiments on a real-world dataset are conducted to validate the advantage of our proposed approach in terms of accuracy and efficiency in recommendation.

scholarly journals Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering

2021 ◽  
Vol 14 (2) ◽  
pp. 26
Author(s):  
Na Li ◽  
Lianguan Huang ◽  
Yanling Li ◽  
Meng Sun
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Clustering Algorithm ◽  
Privacy Preserving ◽  
Locality Sensitive Hashing ◽  
Sensitive Information ◽  
The Public ◽  
Big Data Mining ◽  
Euclidean Distances ◽  
Computational Resources

In recent years, with the development of the Internet, the data on the network presents an outbreak trend. Big data mining aims at obtaining useful information through data processing, such as clustering, clarifying and so on. Clustering is an important branch of big data mining and it is popular because of its simplicity. A new trend for clients who lack of storage and computational resources is to outsource the data and clustering task to the public cloud platforms. However, as datasets used for clustering may contain some sensitive information (e.g., identity information, health information), simply outsourcing them to the cloud platforms can't protect the privacy. So clients tend to encrypt their databases before uploading to the cloud for clustering. In this paper, we focus on privacy protection and efficiency promotion with respect to k-means clustering, and we propose a new privacy-preserving multi-user outsourced k-means clustering algorithm which is based on locality sensitive hashing (LSH). In this algorithm, we use a Paillier cryptosystem encrypting databases, and combine LSH to prune off some unnecessary computations during the clustering. That is, we don't need to compute the Euclidean distances between each data record and each clustering center. Finally, the theoretical and experimental results show that our algorithm is more efficient than most existing privacy-preserving k-means clustering.

Development of the Personal Qualities Assessment as a Tool for Selecting Medical Students

2005 ◽  
Vol 11 (1) ◽  
pp. 3-14 ◽  
Author(s):  
David Powis ◽  
Miles Bore ◽  
Donald Munro ◽  
Mary Ann Lumsden
Keyword(s):  
Cognitive Skills ◽  
Past Research ◽  
Test Results ◽  
Ethical Practice ◽  
Medical Student Selection ◽  
Selection Decisions ◽  
Wide Range ◽  
Test Component ◽  
Personal Qualities ◽  
High Level

A review of the medical student selection literature and our own past research (Lowe, Kerridge, Bore, Munro and Powis (2001) has indicated that competent and ethical practice of medicine requires doctors to possess a range of personal qualities in addition to high-level academic ability. A three-part test battery called the Personal Qualities Assessment (PQA) has been developed as a measure of some of these qualities: it consists of the Mental Agility Test (MAT), which measures cognitive skills, the Mojac scale, which measures moral orientation, and the NACE scale, which measures Narcissism, Aloofness, Confidence and Empathy. Five hundred and seven applicants for entry in October 2003 to the Scottish medical schools volunteered to complete the MAT, Mojac and NACE tests in January 2003. The test results played no part in making or informing selection decisions. The scores obtained by the candidates on each test covered a wide range, indicating that each test component has good discriminating power. Correlations between the test components were low (−0.02 to +0.17), indicating that they each measure different attributes. Since the test results were not used in making selection decisions it will be possible to relate outcome indices (e.g., examination and professional performance as the students progress through medical school) to the test component scores to seek evidence for the predictive validity of the PQA battery and thereby indicate its potential usefulness as a selection tool.

Application-Enabled Collaborative Networking

2015 ◽  
pp. 119-136
Author(s):  
Tirumaleswar Reddy ◽  
Prashanth Patil ◽  
Anca Zamfir
Keyword(s):  
Flow Characteristics ◽  
User Preferences ◽  
Deep Packet Inspection ◽  
User Privacy ◽  
Network Nodes ◽  
Wide Range ◽  
Collaborative Networking ◽  
Adaptive Time ◽  
Packet Inspection ◽  
Flow Treatment

Identification and treatment of application flows are important to many application providers and network operators. They often rely on these capabilities to deploy and/or support a wide range of applications. These applications generate flows that may have specific characteristics such as bandwidth or latency that can be met if made known to the network. Historically, this functionality has been implemented to the extent possible using heuristics that inspect and infer flow characteristics. Heuristics may be based on port numbers, network identifiers (e.g., subnets or VLANs, Deep Flow Inspection (DFI), or Deep Packet Inspection (DPI)). However, many application flows in current usages are dynamic, adaptive, time-bound, encrypted, peer-to-peer (P2P), asymmetric, used on multipurpose devices, and/ or have different priorities depending on the direction of the flow, user preferences, and other factors. Any combination of these properties renders heuristic-based techniques less effective and may result in compromises to application security or user privacy. Application-enabled collaborative networking (AECN) is a framework in which applications explicitly signal their flow characteristics and requirements to the network. This provides network nodes with knowledge of the application flow characteristics, which enables them to apply the correct flow treatment and provide feedback to applications accordingly. This chapter describes how an application enabled collaborative networking framework contributes to solve the encountered problems.

A Mobile Service Recommendation System Using Multi-Criteria Ratings

2010 ◽  
Vol 2 (4) ◽  
pp. 30-40 ◽  
Author(s):  
Zhuang Shao ◽  
Zhikui Chen ◽  
Xiaodi Huang
Keyword(s):  
Recommendation System ◽  
Information Overload ◽  
Mobile Services ◽  
Rank Aggregation ◽  
Mobile Service ◽  
Service Recommendation ◽  
Service Creation ◽  
Multi Agent ◽  
Individual Criterion ◽  
Service Ranking

With the rapid advancement of wireless technologies and mobile devices, mobile services offer great convenience and huge opportunities for service creation. However, information overload make service recommendation become a crucial issue in mobile services. Although traditional single-criteria recommendation systems have been successful in a number of personalization applications, obviously individual criterion cannot satisfy consumers’ demands. Relying on multi-criteria ratings, this paper presents a novel recommendation system using the multi-agent technology. In this system, the ratings with respect to the three criteria are aggregated into an overall service ranking list by a rank aggregation algorithm. Furthermore, all of the services are classified into several clusters to reduce information overload further. Finally, Based on multi-criteria rank aggregation, the prototype of a recommendation system is implemented. Successful applications of this recommendation system have demonstrated the efficiency of the proposed approach.

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