Structural Balance Theory-based E-commerce Recommendation over Big Rating Data

Structural Balance Theory-based E-commerce Recommendation over Big Rating Data

ABSTRACT:

Recommending appropriate product items to the target user is becoming the key to ensure continuous success of Ecommerce. Today, many E-commerce systems adopt various recommendation techniques, e.g., Collaborative Filtering (abbreviated as CF)-based technique, to realize product item recommendation. Overall, the present CF recommendation can perform very well, if the target user owns similar friends (user-based CF), or the product items purchased and preferred by target user own one or more similar product items (item-based CF). While due to the sparsity of big rating data in E-commerce, similar friends and similar product items may be both absent from the user-product purchase network, which lead to a big challenge to recommend appropriate product items to the target user. Considering the challenge, we put forward a Structural Balance Theory-based Recommendation (i.e., SBT-Rec) approach. In the concrete, (Ⅰ) user-based recommendation: we look for target user’s “enemy” (i.e., the users having opposite preference with target user); afterwards, we determine target user’s “possible friends”, according to “enemy’s enemy is a friend” rule of Structural Balance Theory, and recommend the product items preferred by “possible friends” of target user to the target user. (Ⅱ) likewise, for the product items purchased and preferred by target user, we determine their “possibly similar product items” based on Structural Balance Theory and recommend them to the target user. At last, the feasibility of SBT-Rec is validated, through a set of experiments deployed on MovieLens-1M dataset.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Lianyong Qi, Xiaolong Xu, Xuyun Zhang, Wanchun Dou, Chunhua Hu, Yuming Zhou, Jiguo Yu, “Structural Balance Theory-based E-commerce Recommendation over Big Rating Data”, IEEE Transactions on Big Data, 2018.

AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers

ABSTRACT:

In this paper, a set of accountable protocols denoted as AccountTrade is proposed for big data trading among dishonest consumers. For achieving secure the big data trading environment, AccountTrade achieves book-keeping ability and accountability against dishonest consumers throughout the trading (i.e., buying and selling) of datasets. We investigate the consumers’ responsibilities in the dataset trading, then we design AccountTrade to achieve accountability against dishonest consumers that are likely to deviate from the responsibilities. Specifically, a uniqueness index is defined and proposed, which is a new rigorous measurement of the data uniqueness for this purpose. Furthermore, several accountable trading protocols are presented to enable data brokers to blame the misbehaving entities when misbehavior is detected. The accountability of AccountTrade is formally defined, proved, and evaluated by an automatic verification tool as well as extensive simulation with real-world datasets. Our evaluation shows that AccountTrade incurs at most 10KB storage overhead per file, and it is capable of 8-1000 concurrent data upload requests per server.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Taeho Jung, Member, IEEE, Xiang-Yang Li, Fellow, IEEE, Wenchao Huang, Zhongying Qiao, Jianwei Qian, Linlin Chen, Junze Han, Jiahui Hou, “AccountTrade: Accountability Against Dishonest Big Data Buyers and Sellers”, IEEE Transactions on Information Forensics and Security, 2018.

A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds

A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds

ABSTRACT:

Due to the complexity and volume, outsourcing ciphertexts to a cloud is deemed to be one of the most effective approaches for big data storage and access. Nevertheless, verifying the access legitimacy of a user and securely updating a ciphertext in the cloud based on a new access policy designated by the data owner are two critical challenges to make cloud-based big data storage practical and effective. Traditional approaches either completely ignore the issue of access policy update or delegate the update to a third party authority; but in practice, access policy update is important for enhancing security and dealing with the dynamism caused by user join and leave activities. In this paper, we propose a secure and verifiable access control scheme based on the NTRU cryptosystem for big data storage in clouds. We first propose a new NTRU decryption algorithm to overcome the decryption failures of the original NTRU, and then detail our scheme and analyze its correctness, security strengths, and computational efficiency. Our scheme allows the cloud server to efficiently update the ciphertext when a new access policy is specified by the data owner, who is also able to validate the update to counter against cheating behaviors of the cloud. It also enables (i) the data owner and eligible users to effectively verify the legitimacy of a user for accessing the data, and (ii) a user to validate the information provided by other users for correct plaintext recovery. Rigorous analysis indicates that our scheme can prevent eligible users from cheating and resist various attacks such as the collusion attack.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Chunqiang Hu, Member, IEEE, Wei Li, Member, IEEE, Xiuzhen Cheng, Fellow, IEEE Jiguo Yu, Member, IEEE, Shengling Wang, Member, IEEE, and Rongfang Bie, Member, IEEE, “A Secure and Verifiable Access Control Scheme for Big Data Storage in Clouds”, IEEE Transactions on Big Data, 2018.

Lightweight Fine-Grained Search over Encrypted Data in Fog Computing

Lightweight Fine-Grained Search over Encrypted Data in Fog Computing

ABSTRACT:

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift partial computational and storage overhead from end users to chosen fog nodes. Furthermore, the basic LFGS system is improved to support conjunctive keyword search and attribute update to avoid returning irrelevant search results and illegal accesses. The formal security analysis shows that the LFGS system can resist Chosen-Keyword Attack (CKA) and Chosen-Plaintext Attack (CPA), and the simulation using a real-world dataset demonstrates that the LFGS system is efficient and feasible in practice.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Yinbin Miao, Jianfeng Ma, Ximeng Liu, Jian Weng, Hongwei Li, and Hui Li, “Lightweight Fine-Grained Search over Encrypted Data in Fog Computing”, IEEE Transactions on Services Computing, 2018.

 

A Developer Centered Bug Prediction Model

A Developer Centered Bug Prediction Model

ABSTRACT:

Several techniques have been proposed to accurately predict software defects. These techniques generally exploit characteristics of the code artefacts (e.g., size, complexity, etc.) and/or of the process adopted during their development and maintenance (e.g., the number of developers working on a component) to spot out components likely containing bugs. While these bug prediction models achieve good levels of accuracy, they mostly ignore the major role played by human-related factors in the introduction of bugs. Previous studies have demonstrated that focused developers are less prone to introduce defects than non-focused developers. According to this observation, software components changed by focused developers should also be less error prone than components changed by less focused developers. We capture this observation by measuring the scattering of changes performed by developers working on a component and use this information to build a bug prediction model. Such a model has been evaluated on 26 systems and compared with four competitive techniques. The achieved results show the superiority of our model, and its high complementarity with respect to predictors commonly used in the literature. Based on this result, we also show the results of a “hybrid” prediction model combining our predictors with the existing ones.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Dario Di Nucci, Fabio Palomba, Giuseppe De Rosa, “A Developer Centered Bug Prediction Model”, IEEE Transactions on Software Engineering, 2018.

Query-free Clothing Retrieval via Implicit Relevance Feedback

Query-free Clothing Retrieval via Implicit Relevance Feedback

ABSTRACT:

Image-based clothing retrieval is receiving increasing interest with the growth of online shopping. In practice, users may often have a desired piece of clothing in mind (e.g., either having seen it before on the street or requiring certain specific clothing attributes) but may be unable to supply an image as a query. We model this problem as a new type of image retrieval task in which the target image resides only in the user’s mind (called “mental image retrieval” hereafter). Because of the absence of an explicit query image, we propose to solve this problem through relevance feedback. Specifically, a new Bayesian formulation is proposed that simultaneously models the retrieval target and its high-level representation in the mind of the user (called the “user metric” hereafter) as posterior distributions of pre-fetched shop images and heterogeneous features extracted from multiple clothing attributes, respectively. Requiring only clicks as user feedback, the proposed algorithm is able to account for the variability in human decision-making. Experiments with real users demonstrate the effectiveness of the proposed algorithm.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Zhuoxiang Chen, Zhe Xu, Ya Zhang, Member, IEEE, and Xiao Gu, “Query-free Clothing Retrieval via Implicit Relevance Feedback”, IEEE Transactions on Multimedia, 2018.

Joint Hypergraph Learning for Tag-based Image Retrieval

Joint Hypergraph Learning for Tag-based Image Retrieval

ABSTRACT:

As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval (TBIR). It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated. However, most existing methods use these visual features separately or sequentially. In this paper, we propose a global and local visual features fusion approach to learn the relevance of images by hypergraph approach. A hypergraph is constructed first by utilizing global, local visual features and tag information. Then, we propose a pseudo-relevance feedback mechanism to obtain the pseudo-positive images. Finally, with the hypergraph and pseudo relevance feedback, we adopt the hypergraph learning algorithm to calculate the relevance score of each image to the query. Experimental results demonstrate the effectiveness of the proposed approach.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Yaxiong Wang, Li Zhu, Xueming Qian, Member, IEEE, Junwei Han, “Joint Hypergraph Learning for Tag-based Image Retrieval”, IEEE Transactions on Image Processing, 2018.

MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things

MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things

ABSTRACT:

Cloning is a very serious threat in the Internet of Things (IoT), owing to the simplicity for an attacker to gather configuration and authentication credentials from a non-tamper-proof node, and replicate it in the network. In this paper, we propose MDSClone, a novel clone detection method based on multidimensional scaling (MDS). MDSClone appears to be very well suited to IoT scenarios, as it (i) detects clones without the need to know the geographical positions of nodes, and (ii) unlike prior methods, it can be applied to hybrid networks that comprise both static and mobile nodes, for which no mobility pattern may be assumed a priori. Moreover, a further advantage of MDSClone is that (iii) the core part of the detection algorithm can be parallelized, resulting in an acceleration of the whole detection mechanism. Our thorough analytical and experimental evaluations demonstrate that MDSClone can achieve a 100% clone detection probability. Moreover, we propose several modifications to the original MDS calculation, which lead to over a 75% speed up in large scale scenarios. The demonstrated efficiency of MDSClone proves that it is a promising method towards a practical clone detection design in IoT.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Po-Yen Lee, Chia-Mu Yu, Tooska Dargahi, Mauro Conti, IEEE Senior Member, and Giuseppe Bianchi, “MDSClone: Multidimensional Scaling Aided Clone Detection in Internet of Things”, IEEE Transactions on Information Forensics and Security, 2018.

Light-Weight Security and Data Provenance for Multi-Hop Internet of Things

Light-Weight Security and Data Provenance for Multi-Hop Internet of Things

ABSTRACT:

Due to the limited resources and scalability, the security protocols for the Internet of Things (IoT) need to be light-weighted. The cryptographic solutions are not feasible to apply on small and low-energy devices of IoT because of their energy and space limitations. In this paper, a light-weight protocol to secure the data and achieving data provenance is presented for the multi-hop IoT network. The Received Signal Strength Indicator (RSSI) of communicating IoT nodes are used to generate the link fingerprints. The link fingerprints are matched at the server to compute the correlation coefficient. Higher the value of correlation coefficient, higher the percentage of the secured data transfers. Lower value gives the detection of adversarial node in between a specific link. Data provenance has also been achieved by comparison of packet header with all the available link fingerprints at the server. The time complexity is computed at the node and server level, which is O(1). The energy dissipation is calculated for the IoT nodes and overall network. The results show that the energy consumption of the system presented in this paper is 52_53 mJ for each IoT node and 313.626 mJ for the entire network. The RSSI values are taken in real time from MICAz motes and simulations are performed on MATLAB for adversarial node detection, data provenance, and time-complexity. Experimental results show that up to 97% correlation is achieved when no adversarial node is present in the IoT network.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

MOHSIN KAMAL , (Member, IEEE), AND MUHAMMAD TARIQ, “Light-Weight Security and Data Provenance for Multi-Hop Internet of Things”, IEEE Access, 2018.

 

Cloud-based Fine-grained Health Information Access Control Framework for Lightweight IoT Devices with Dynamic Auditing and Attribute Revocation

Cloud-based Fine-grained Health Information Access Control Framework for Lightweight IoT Devices with Dynamic Auditing and Attribute Revocation

ABSTRACT:

The eHealth trend has spread globally. Internet of Things (IoT) devices for medical service and pervasive Personal Health Information (PHI) systems play important roles in the eHealth environment. A cloud-based PHI system appears promising but raises privacy and information security concerns. We propose a cloud-based fine-grained health information access control framework for lightweight IoT devices with data dynamics auditing and attribute revocation functions. Only symmetric cryptographyis required for IoT devices, such as wireless body sensors. A variant of cipher text-policy attribute-based encryption, dual encryption, and Merkle hash trees are used to support fine-grained access control, efficient dynamic data auditing, batch auditing, and attribute revocation. Moreover, the proposed scheme also defines and handles the cloud reciprocity problem wherein cloud service providers can help each other avoid fines resulting from data loss. Security analysis and performance comparisons show that the proposed scheme is an excellent candidate for a cloud-based PHI system.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS: 

  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB

SOFTWARE REQUIREMENTS: 

  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL

REFERENCE:

Lo-Yao Yeh, Pei-Yu Chiang, Yi-Lang Tsai, and Jiun-Long Huang, IEEE Member, “Cloud-based Fine-grained Health Information Access Control Framework for Lightweight IoT Devices with Dynamic Auditing and Attribute Revocation”,   IEEE Transactions on Cloud Computing, 2018.