Trust-based Collaborative Privacy Management in Online Social Networks

Trust-based Collaborative Privacy Management in Online Social Networks

ABSTRACT:

Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals’ privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users’ privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users’ opinions, and the values are updated according to users’ privacy loss. Moreover, the user can make a trade-off between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others’ privacy, and the proposed bandit approach can bring the user a high payoff.

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:

Lei Xu1, Chunxiao Jiang2, Nengqiang He3, Zhu Han4 and Abderrahim Benslimane, “Trust-based Collaborative Privacy Management in Online Social Networks”, IEEE Transactions on Information Forensics and Security, 2018.

Exploiting Social Network to Enhance Human-to-Human Infection Analysis Without Privacy Leakage

Exploiting Social Network to Enhance Human-to-Human Infection Analysis Without Privacy Leakage

ABSTRACT:

Human-to-human infection, as a type of fatal public health threats, can rapidly spread in a human population, resulting in a large amount of labor and health cost for treatment, control and prevention. To slow down the spread of infection, social network is envisioned to provide detailed contact statistics to isolate susceptive people who has frequent contacts with infected patients. In this paper, we propose a novel human-to-human infection analysis approach by exploiting social network data and health data that are collected by social network and e-healthcare technologies. We enable the social cloud server and health cloud server to exchange social contact information of infected patients and user’s health condition in a privacy-preserving way. Specifically, we propose a privacy-preserving data query method based on conditional oblivious transfer to guarantee that only the authorized entities can query users’ social data and the social cloud server cannot infer anything during the query. In addition, we propose a privacy-preserving classification-based infection analysis method that can be performed by untrusted cloud servers without accessing the users’ health data. The performance evaluation shows that the proposed approach achieves higher infection analysis accuracy with the acceptable computational overhead.

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:

Kuan Zhang, Student Member, IEEE, Xiaohui Liang, Member, IEEE, Jianbing Ni, Kan Yang, and Xuemin (Sherman) Shen Fellow, IEEE, “Exploiting Social Network to Enhance Human-to-Human Infection Analysis Without Privacy Leakage”, IEEE Transactions on Dependable and Secure Computing, 2018.

Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks

ABSTRACT:

Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, it is possible to predict sensitive information carried in released data latently by utilizing data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. The key novel idea lies that besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary’s prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. To the best of our knowledge, this is the first work that employs collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.

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:

Zhipeng Cai, Senior Member, IEEE, Zaobo He, Xin Guan, and Yingshu Li, Senior Member, IEEE, “Collective Data-Sanitization for Preventing Sensitive Information Inference Attacks in Social Networks”, IEEE Transactions on Dependable and Secure Computing, 2018.

A Provably-Secure Cross-Domain Handshake Scheme with Symptoms-Matching for Mobile Healthcare Social Network

A Provably-Secure Cross-Domain Handshake Scheme with Symptoms-Matching for Mobile Healthcare Social Network

ABSTRACT:

With rapid developments of sensor, wireless and mobile communication technologies, Mobile Healthcare Social Networks (MHSNs) have emerged as a popular means of communication in healthcare services. Within MHSNs, patients can use their mobile devices to securely share their experiences, broaden their understanding of the illness or symptoms, form a supportive network, and transmit information (e.g. state of health and new symptoms) between users and other stake holders (e.g. medical center). Despite the benefits afforded by MHSNs, there are underlying security and privacy issues (e.g. due to the transmission of messages via a wireless channel). The handshake scheme is an important cryptographic mechanism, which can provide secure communication in MHSNs (e.g. anonymity and mutual authentication between users, such as patients). In this paper, we present a new framework for the handshake scheme in MHSNs, which is based on hierarchical identity-based cryptography. We then construct an efficient Cross-Domain HandShake (CDHS) scheme that allows symptoms-matching within MHSNs. For example, using the proposed CDHS scheme, two patients registered with different healthcare centers can achieve mutual authentication and generate a session key for future secure communications. We then prove the security of the scheme, and a comparative summary demonstrates that the proposed CDHS scheme requires fewer computation and lower communication costs. We also implement the proposed CDHS scheme and three related schemes in a proof of concept Android app to demonstrate utility of the scheme. Findings from the evaluations demonstrate that the proposed CDHS scheme achieves a reduction of 18.14% and 5.41% in computation cost and communication cost, in comparison to three other related handshake schemes.

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:

Debiao He, Neeraj Kumar, Huaqun Wang, Lina Wang, Kim-Kwang Raymond Choo, Alexey Vinel, “A Provably-Secure Cross-Domain Handshake Scheme with Symptoms-Matching for Mobile Healthcare Social Network”, IEEE Transactions on Dependable and Secure Computing, 2018.

A Hybrid Approach for Detecting Automated Spammers in Twitter

A Hybrid Approach for Detecting Automated Spammers in Twitter

ABSTRACT:

Twitter is one of the most popular microblogging services, which is generally used to share news and updates through short messages restricted to 280 characters. However, its open nature and large user base are frequently exploited by automated spammers, content polluters, and other ill-intended users to commit various cyber crimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of approaches have been proposed by researchers to address these problems. However, most of these approaches are based on user characterization and completely disregarding mutual interactions. In this study, we present a hybrid approach for detecting automated spammers by amalgamating community based features with other feature categories, namely metadata- , content-, and interaction-based features. The novelty of the proposed approach lies in the characterization of users based on their interactions with their followers given that a user can evade features that are related to his/her own activities, but evading those based on the followers is difficult. Nineteen different features, including six newly defined features and two redefined features, are identified for learning three classifiers, namely, random forest, decision tree, and Bayesian network, on a real dataset that comprises benign users and spammers. The discrimination power of different feature categories is also analyzed, and interaction- and community-based features are determined to be the most effective for spam detection, whereas metadata-based features are proven to be the least effective.

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:

Mohd Fazil and Muhammad Abulaish, Senior Member, IEEE, “A Hybrid Approach for Detecting Automated Spammers in Twitter”, IEEE Transactions on Information Forensics and Security, 2018.

Relevant Feedback Based Accurate and Intelligent Retrieval on Capturing User Intention for Personalized Websites

Relevant Feedback Based Accurate and Intelligent Retrieval on Capturing User Intention for Personalized Websites

ABSTRACT:

With the rapid growth of networking, cyber_physical_social systems (CPSSs) provide vast amounts of information. Aimed at the huge and complex data provided by networking, obtaining valuable information to meet precise search needs when capturing user intention has become a major challenge, especially in personalized websites. General search engines face difficulties in addressing the challenges brought by this exploding amount of information. In this paper, we use real-time location and relevant feedback technology to design and implement an efficient, configurable, and intelligent retrieval framework for personalized websites in CPSSs. To improve the retrieval results, this paper also proposes a strategy of implicit relevant feedback based on click-through data analysis, which can obtain the relationship between the user query conditions and retrieval results. Finally, this paper designs a personalized PageRank algorithm including modified parameters to improve the ranking quality of the retrieval results using the relevant feedback from other users in the interest group. Experiments illustrate that the proposed accurate and intelligent retrieval framework improves the user experience.

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:

YAYUAN TANG, HAO WANG, KEHUA GUO, YIZHE XIAO, AND TAO CHI, “Relevant Feedback Based Accurate and Intelligent Retrieval on Capturing User Intention for Personalized Websites”, IEEE Access,2018.

Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud Computing

Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud Computing

ABSTRACT:

Mobile cloud computing is an emerging cloud computing paradigm that integrates cloud computing and mobile computing to enable many useful mobile applications. However, the large-scale deployment of mobile cloud computing is hindered by the concerns on possible privacy leakage. In this paper, we investigate the privacy issues in the ad hoc mobile cloud computing, and propose a framework that can protect the location privacy when allocating tasks to mobile devices. Our mechanism is based on differential privacy and geocast, and allows mobile devices to contribute their resources to the ad hoc mobile cloud without leaking their location information. We develop analytical models and task allocation strategies that balance privacy, utility, and system overhead in an ad hoc mobile cloud. We also conduct extensive experiments based on real-world datasets, and the results show that our framework can protect location privacy for mobile devices while providing effective services with low system overhead.

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:

Yanmin Gong, Student Member, IEEE, Chi Zhang, Member, IEEE, Yuguang Fang, Fellow, IEEE, and Jinyuan Sun, Member, IEEE, “Protecting Location Privacy for Task Allocation in Ad Hoc Mobile Cloud Computing”, IEEE Transactions on Emerging Topics in Computing, 2018.

Identifying On-site Users for Social Events: Mobility, Content, and Social Relationship

Identifying On-site Users for Social Events: Mobility, Content, and Social Relationship

ABSTRACT:

The wide spread use of social network services, especially location based services, has transformed social networks into an important information source of real-world events. Many event detection systems using geo-tagged posts from social networks have been developed in recent years. Besides detecting real-world events, it is also desirable for government officials, news media, and police etc. to identify on-site users of an event, from whom we could gather valuable information regarding the process of events and investigate suspects when an event is associated with crime or terrorist. However, due to the high uncertainty of human mobility patterns and the low probability of users sharing their location information, it is difficult to identify on-site users while a social event unfolds, and research work in this area is still in its infancy. In this paper, we propose a Fused fEature Gaussian prOcess Regression (FEGOR) model, which exploits three influential factors in social networks for on-site user identification: mobility influence, content similarity, and social relationship. By integrating these factors, we are able to estimate the distance between a user and a social event even when the user’s location profile is unknown, thus identify on-site users. Experiments on a real-world Twitter dataset demonstrate the effectiveness of our model, achieving a minimum mean absolute error of 1.7km and outperforming state-of-the-art methods.

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:

Zhiwen Yu, Senior Member, IEEE, Fei Yi, Qin Lv, Bin Guo, Senior Member, IEEE, “Identifying On-site Users for Social Events: Mobility, Content, and Social Relationship”, IEEE Transactions on Mobile Computing, 2018.

Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services

Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services

ABSTRACT:

With the exponential increase of the mobile devices and the fast development of cloud computing, a new computing paradigm called mobile cloud computing (MCC) is put forward to solve the limitation of the mobile device’s storage, communication, and computation. Through mobile devices, users can enjoy various cloud computing services during their mobility. However, it is difficult to ensure security and protect privacy due to the openness of wireless communication in the new computing paradigm. Recently, Tsai and Lo proposed a privacy-aware authentication (PAA) scheme to solve the identification problem in MCC services and proved that their scheme was able to resist many kinds of existing attacks. Unfortunately, we found that Tsai and Lo’s scheme cannot resist the service provider impersonation attack, i.e., an adversary can impersonate the service provider to the user. Also, the adversary can extract the user’s real identity during executing the service provider impersonation attack. To address the above problems, in this paper, we construct a new PAA scheme for MCC services by using an identity-based signature scheme. Security analysis shows that the proposed PAA scheme is able to address the serious security problems existing in Tsai and Lo’s scheme and can meet security requirements for MCC services. The performance evaluation shows that the proposed PAA scheme has less computation and communication costs compared with Tsai and Lo’s PAA scheme.

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:

Debiao He, Neeraj Kumar, Muhammad Khurram Khan, Lina Wang, and Jian Shen, “Efficient Privacy-Aware Authentication Scheme for Mobile Cloud Computing Services”, IEEE SYSTEMS JOURNAL, 2018.

Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis

Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis

ABSTRACT:

Propagation of faulty data is a critical issue. In case of Delay Tolerant Networks (DTN) in particular, the rare meeting events require that nodes are efficient in propagating only correct information. For that purpose, mechanisms to rapidly identify possible faulty nodes should be developed. Distributed faulty node detection has been addressed in the literature in the context of sensor and vehicular networks, but already proposed solutions suffer from long delays in identifying and isolating nodes producing faulty data. This is unsuitable to DTNs where nodes meet only rarely. This paper proposes a fully distributed and easily implementable approach to allow each DTN node to rapidly identify whether its sensors are producing faulty data. The dynamical behavior of the proposed algorithm is approximated by some continuous-time state equations, whose equilibrium is characterized. The presence of misbehaving nodes, trying to perturb the faulty node detection process, is also taken into account. Detection and false alarm rates are estimated by comparing both theoretical and simulation results. Numerical results assess the effectiveness of the proposed solution and can be used to give guidelines for the algorithm design.

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:

Wenjie Li, Student Member, IEEE, Laura Galluccio, Member, IEEE, Francesca Bassi, Member, IEEE, and Michel Kieffer , Senior Member, IEEE, “Distributed Faulty Node Detection in Delay Tolerant Networks: Design and Analysis”, IEEE TRANSACTIONS ON MOBILE COMPUTING, VOL. 17, NO. 4, APRIL 2018.