The development of several popular social networks and the publication of social networks’ data have led to the risk of leakage of sensitive and confidential information of individuals. This requires the preservation of privacy before the publication of a user’s data available from his Online Social Network (OSN) presence. Numerous algorithms have been proposed in the area of preserving the privacy of social network users’ information such as K-anonymity and L-diversity. Previous work has shown good results based on the concept of adding edges and noise nodes for achieving K-anonymity and L-diversity. K-anonymization techniques are able to prevent identity disclosure of users but are not sufficient to prevent the disclosure of sensitive information of users. In this direction, a number of techniques for preserving the sensitive information of social network users have been proposed. Although these techniques have shown reasonably good results to achieve anonymity, but they also lead to a substantial change in the original structure of the OSNs. In this article, the problems of preventing sensitive attribute disclosure and reducing the noisy nodes have been addressed by perturbing the sensitive attributes. Existing research uses L-diversity for preventing sensitive attribute disclosure resulting in skewness and similarity attacks. We have addressed the skewness attacks by removing the duplicate noisy nodes from the final dataset to be published for stakeholders by the OSN service providers. All the information of duplicate nodes has been stored in a table named Reference Attribute Table (RAT). This table will be accessible only to the service providers for the purpose of de-anonymizing the data of users. The proposed technique has been extensively evaluated using five metrics viz. APL, ACSPL, RRTI, number of noisy nodes, and information loss using four real-time datasets collected for OSNs namely CORA, ARNET, DBLP, and Twitter. Results of evaluation parameters viz. APL and RRTI show that there is less change in the structure of datasets after anonymization. Results of ACSPL show that our proposed technique is able to preserve sensitive attributes in the datasets. The maximum number of noisy nodes amongst all four datasets is 5.4% and the maximum information loss is 2.2%. Evaluation results make it evident that our proposed technique ensures privacy preservation with less loss of information and thus preserving the utility of published data.

Millions of individuals use Twitter, one of the most prominent social media sites, to exchange information, broadcast tweets, and follow other users. Twitter being an open application programming interface is vulnerable to attacks from fake accounts. Fake accounts are primarily used for advertising and marketing, defamation of an individual, consumer data acquisition, increasing fake blog or website traffic, sharing wrong information, online fraud, and control. Fake accounts are disruptive to both users and service providers, thus it's critical to recognize and filter out such information on social media. This paper presents a technique for detecting fake Twitter accounts using the feature set mapped to Twitter’s rules and policies to flag suspicious accounts. To choose the apt subset of features from the original feature space, feature selection techniques such as information gain and correlation were used. Users have been classified using a Logistic Regression classifier with the detection accuracy of 93.5 percent after being trained on the data of about 1,00,000 Twitter users. Furthermore, an algorithm for classifying suspicious users as Fake profiles has been presented. The results of the experiments show that the suggested model outperforms competitive state-of-the-art research in the majority of circumstances.

With the use of identity resolution, both information leakage and identity hacking can be reduced to some extent. In this paper, a prototype has been developed to classify Twitter users as suspicious and nonsuspicious on the basis of features which identify user demographics and their tweeting activity using Twitter APIs. A model has been devised based upon user and tweet meta-data which is used to calculate user score and tweet score, and further aggregate the values generated by these scores to label suspicious and nonsuspicious users in the collected dataset of around 21,492 Twitter users. Further, support vector machine classifier has been used to classify the labeled data. Through this paper, our analysis about the role of features and the characteristics of dataset used for the categorization of users in Twitter has been reported. The experimental results illustrate that the proposed system can identify suspicious users with an accuracy of 94.1%.

In the arena of internet of things, everyone has the ability to share every aspect of their lives with other people. Social media is the most popular and effective medium to provide communication. Social media has gripped our lives in a dramatic way. Privacy of users data lying with the service providers needs to be preserved when published for the purpose of research as the release of sensitive personal information of an individual may pose security threats. This has become an important research area nowadays. To some extent, the concepts of anonymization that were earlier used to preserve privacy of relational microdata have been applied to preserve privacy of social networks data. Anonymizing social networks data is challenging as it is a complex structure with users connected to one another graphically and the most important is to preserve the structural properties of the graph depicting the social network relationships while applying such concepts. Recent studies based upon K-anonymity and L-diversity help to preserve privacy of online social networks data and subsequently identify attacks that arise while applying these techniques in different scenarios. K-anonymity equalizes the degree of the nodes to prevent the data from identity disclosure but it cannot preserve sensitive information and also cannot handle attacks arising due to background knowledge and homogeneity. To cope up with the drawbacks of K anonymity, L-diversity was introduced that protects the sensitive labels of the users. In this paper, a novel technique has been proposed which implements the combined features of K-anonymity and L-diversity. Our proposed approach has been validated using the data of real time social network–Twitter (most popular microblogging network). The performance of the proposed technique has been measured by the metrics, such as average path length, average change in sensitive labels, and remaining ratio of top influential users. It thus becomes evident from the results that the values of these parameters attained with the proposed technique for the anonymized graph has minimal variation to that of original structural graph. So, it is possible to retain the utility without compromising privacy while publishing social networks data. Further, the performance of the proposed technique has been discussed by calculating the information loss that addresses the concern of preserving privacy with the least variation of actual content viz info loss.

Social networks like Facebook, Twitter, Pinterest etc. provide data of its users to the demanding organizations to better comprehend the quality of their potential clients. Publishing confidential data of social network users in its raw form raises several privacy and security concerns. Recently, some anonymization techniques have been developed to address these issues. In this paper, a technique to prevent identity disclosure through structure attacks has been proposed which not only prevents identity disclosure but also preserves utility of data published by online social networks. Algorithms have been developed by using noise nodes/edges with the consideration of introducing minimum change in the original graphical structure of social networks. The authors' work is unique in the sense that previous works are based on edge editing only but their proposed work protects against structure attacks using mutual nodes in the social network and the effectiveness of the proposed technique has been proved using APL (Average Path Length) and information loss as parameters.

With the increase in online publishing of social network data the requirement to protect confidential information related to users has become the main concern of publishers. To cater to this need many anonymization techniques like K-anonymity, L-diversity and T-closeness has been proposed by various researchers for micro-data as well as social network data. In this paper we aim to protect sensitive information of users of Twitter-second most popular social networking site. For the purpose of carrying out anonymization, a crawler has been developed to collect data of around 10K users from publicly available information. Data of around 30 users have been used to carry out the experimental work using ARX tool. All three anonymization methods: K-anonymity, L-diversity and T-closeness have been used. Performance of technique is evaluated using information gain as a metric.

In this paper a battery fault detection unit is developed using multiple model adaptive estimation technique. Impedance spectroscopy data from Li-ion cell is used along with the equivalent circuit methodology to construct the battery models. Battery faults such as over charge and over discharge cause significant model parameter variation and can be considered as separate models. Kalman filters are used to estimate the parameters of each model and to generate the residual signal. These residuals are used in the multiple model adaptive estimation technique to detect battery faults. Simulation results show that using this method the stated battery faults can be detected in real-time, thus providing an effective way of diagnosing Li-Ion battery failure.

This paper illustrates the application of model predictive control for trajectory control of micro electro mechanical lateral comb resonators (MEMS LCRs). A recursive least square estimator with dynamic data weighting method is used to estimate the parameters of MEMS in real-time. Accordingly, the control law and the gains are derived to achieve high performance trajectory control. Simulation results demonstrate a close trajectory profile tracking and accurate parameter estimation performance.