Cost-Effective App Data Distribution in Edge Computing

Xia, Xiaoyu; Chen, Feifei; He, Qiang; Grundy, John; Abdelrazek, Mohamed; Jin, Hai

doi:10.1109/tpds.2020.3010521

Cited by 140 publications

(55 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In future study, we will continuously refine SIFSimHash by considering more data types [25][26][27][28][29] and more optimization metrics [30][31][32][33][34][35]. Besides, how to integrate SIFSimHash with other privacy protection techniques (e.g., in [36][37][38][39][40][41][42][43][44][45]) is another research focus.…”

Section: Discussionmentioning

confidence: 99%

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Xing³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

With the ever-increasing popularity of sports and health ideas, people are paying more attentions to gaining high-quality healthy life through various taking various sport items or exercises. Through observing and analyzing the past sport exercise score records, we can cluster the players into different categories, each of which share the same or similar sport preferences or performances. However, the sport exercise score records are often massive and often stored in different cloud platforms, which raise a big difficulty for time-efficient player clustering. Furthermore, the sport exercise score records are a kind of privacy for most players; therefore, it is often not rational or legal to release these sensitive data to the public for similar player clustering purpose. Considering the above two issues, we use SimHash, a kind of privacy-aware approximate neighbor search technique, for similar player clustering by analyzing the sport exercise score records distributed across different cloud platforms. Thus, we can realize privacy-aware similar player clustering through SimHash. At last, we provide a set of experiments to validate the advantages of our proposed privacy-aware similar player clustering algorithm. Reported experimental results show the effectiveness of our proposal in remedying the big data volume and privacy concerns in player clustering based on sport exercise score records.

show abstract

Section: Discussionmentioning

confidence: 99%

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Xing³

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…In addition, we will take the time and space factors into consideration to improve the algorithm from many aspects, such as privacy [22][23][24][25]. We will also further improve the performance and effectiveness of the algorithm [26][27][28] by combining some new approaches such as Blockchain and Edge Computing [29][30][31][32].…”

Section: Discussionmentioning

confidence: 99%

Service Recommendation with High Accuracy and Diversity

Shengqi

Kou

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

In recent years, the number of web services grows explosively. With a large amount of information resources, it is difficult for users to quickly find the services they need. Thus, the design of an effective web service recommendation method has become the key factor to satisfy the requirements of users. However, traditional recommendation methods often tend to pay more attention to the accuracy of the results but ignore the diversity, which may lead to redundancy and overfitting, thus reducing the satisfaction of users. Considering these drawbacks, a novel method called DivMTID is proposed to improve the effectiveness by achieving accurate and diversified recommendations. First, we utilize users’ historical scores of web services to explore the users’ preferences. And we use the TF-IDF algorithm to calculate the weight vector of each web service. Second, we utilize cosine similarity to calculate the similarity between candidate web services and historical web services and we also forecast the ranking scores of candidate web services. At last, a diversification method is used to generate the top- K recommended list for users. And through a case study, we show that DivMTID is an effective, accurate, and diversified web service recommendation method.

show abstract

“…Privacy preservation using DNNs LAYENT [8] Modified CNN inference module [9] ObfNet [10] Privacy partition [11] [12] Homomorphic encryption [5] Additive perturbation [6] Multiplication perturbation [7] Figure 2: Structure of this section.…”

Section: Data Obfuscationmentioning

confidence: 99%

“…To solve the abovementioned bottleneck, a new computing paradigm called edge computing (EC) has been proposed recently and gets widespread attention. EC refers to the technology that deploys computing tasks to the edge of the network [5,6]. Compared with cloud computing, EC has many advantages, including protecting end-users' privacy, reducing the latency while data transmission, decreasing the burden of network bandwidth, and lessening the energy consumption of data center.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey

Liu

Huang

et al. 2020

Security and Communication Networks

View full text Add to dashboard Cite

With the explosive growth of data generated by the Internet of Things (IoT) devices, the traditional cloud computing model by transferring all data to the cloud for processing has gradually failed to meet the real-time requirement of IoT services due to high network latency. Edge computing (EC) as a new computing paradigm shifts the data processing from the cloud to the edge nodes (ENs), greatly improving the Quality of Service (QoS) for those IoT applications with low-latency requirements. However, compared to other endpoint devices such as smartphones or computers, distributed ENs are more vulnerable to attacks for restricted computing resources and storage. In the context that security and privacy preservation have become urgent issues for EC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges. The powerful learning ability of AI enables the system to identify malicious attacks more accurately and efficiently. Meanwhile, to a certain extent, transferring model parameters instead of raw data avoids privacy leakage. In this paper, a comprehensive survey of the contribution of AI to the IoT security in EC is presented. First, the research status and some basic definitions are introduced. Next, the IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services with AI is then presented. In the end, the open issues and challenges on the application of AI in IoT services based on EC are discussed.

show abstract

Cost-Effective App Data Distribution in Edge Computing

Cited by 140 publications

References 38 publications

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

A Novel Similar Player Clustering Method With Privacy Preservation for Sport Performance Evaluation in Cloud

Service Recommendation with High Accuracy and Diversity

Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey

Contact Info

Product

Resources

About