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

Xu, Yanwei; Qi, Lianyong; Dou, Wanchun; Yu, Jiguo

doi:10.1155/2017/3437854

Cited by 96 publications

(75 citation statements)

References 26 publications

(27 reference statements)

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“…Some of the interesting problems are how to adjust subsequence length/ step length and regularize a network and how to use LSTM-FCN, ALSTM-FCN [9], hierarchies of feature [19], or time aware [20,21] to improve the performance of time series classification. In addition, we will try to apply the method of classification service in distributed cloud/fog environment [22][23][24][25][26][27][28][29][30][31][32] in the future.…”

Section: Discussionmentioning

confidence: 99%

Time series classification based on statistical features

Lei

2020

J Wireless Com Network

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This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC. This method is based on fully convolutional neural networks (FCN), and there are the following two properties: statistical features in data preprocessing and finetuning strategies in network training. The key steps are described as follows: firstly, by the window slicing principle, dividing the original time series into multiple equal-length subsequences; secondly, by extracting statistical features on each subsequence, in order to form a new sequence as the input of the neural network, and training neural network by the fine-tuning idea; thirdly, by evaluating the classification performance about test sets; and finally, by comparing the sample sequence complexity and network classification loss accuracy with the FCN using the original sequence. Our experimental results show that the proposed method improved the classification effects of FCN and the residual network (ResNet), which means that it has a generalization ability to the network structures.

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Section: Discussionmentioning

confidence: 99%

Time series classification based on statistical features

Lei

2020

J Wireless Com Network

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“…In our previous work [19,20,21], LSH is combined with user-based CF to make privacy-preserving service recommendation. Likewise, in [22], LSH is combined with item-based CF to build service index table with little privacy and then make service recommendation based on the service index table.…”

Section: Related Workmentioning

confidence: 99%

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

Meng

Zhang

et al. 2018

Sensors

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Service recommendation has become an effective way to quickly extract insightful information from massive data. However, in the cloud environment, the quality of service (QoS) data used to make recommendation decisions are often monitored by distributed sensors and stored in different cloud platforms. In this situation, integrating these distributed data (monitored by remote sensors) across different platforms while guaranteeing user privacy is an important but challenging task, for the successful service recommendation in the cloud environment. Locality-Sensitive Hashing (LSH) is a promising way to achieve the abovementioned data integration and privacy-preservation goals, while current LSH-based recommendation studies seldom consider the possible recommendation failures and hence reduce the robustness of recommender systems significantly. In view of this challenge, we develop a new LSH variant, named converse LSH, and then suggest an exception handling approach for recommendation failures based on the converse LSH technique. Finally, we conduct several simulated experiments based on the well-known dataset, i.e., Movielens to prove the effectiveness and efficiency of our approach.

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“…The privacy preservation is the main concern of cloud application, such as service recommendation [5][6][7], service quality prediction [8,9], database query [10][11][12][13][14][15][16] etc. As an important research branch, the privacy-preserving database query (PPDQ) aims to protect database security and clients' privacy, while ensuring the correctness of database query.…”

Section: Introductionmentioning

confidence: 99%

A Quantum-Based Database Query Scheme for Privacy Preservation in Cloud Environment

Liu

Gao

Liu

et al. 2019

Security and Communication Networks

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Cloud computing is a powerful and popular information technology paradigm that enables data service outsourcing and provides higher-level services with minimal management effort. However, it is still a key challenge to protect data privacy when a user accesses the sensitive cloud data. Privacy-preserving database query allows the user to retrieve a data item from the cloud database without revealing the information of the queried data item, meanwhile limiting user's ability to access other ones. In this study, in order to achieve the privacy preservation and reduce the communication complexity, a quantum-based database query scheme for privacy preservation in cloud environment is developed. Specifically, all the data items of the database are firstly encrypted by different keys for protecting server's privacy, and in order to guarantee the clients' privacy, the server is required to transmit all these encrypted data items to the client with the oblivious transfer strategy. Besides, two oracle operations, a modified Grover iteration, and a special offset encryption mechanism are combined together to ensure that the client can correctly query the desirable data item. Finally, performance evaluation is conducted to validate the correctness, privacy, and efficiency of our proposed scheme.

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

Cited by 96 publications

References 26 publications

Time series classification based on statistical features

Time series classification based on statistical features

An Exception Handling Approach for Privacy-Preserving Service Recommendation Failure in a Cloud Environment

A Quantum-Based Database Query Scheme for Privacy Preservation in Cloud Environment

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