Biblio

Many people are becoming more reliant on internet social media sites like Facebook. Users can utilize these networks to reveal articles to them and engage with your peers. Several of the data transmitted from these connections is intended to be confidential. However, utilizing publicly available data and learning algorithms, it is feasible to forecast concealed informative data. The proposed research work investigates the different ways to initiate deduction attempts on freely released photo sharing data in order to envisage concealed informative data. Next, this research study offers three distinct sanitization procedures that could be used in a range of scenarios. Moreover, the effectualness of all these strategies and endeavor to utilize collective teaching and research to reveal important bits of the data set are analyzed. It shows how, by using the sanitization methods presented here, a user may lower the accuracy by including both global and interpersonal categorization techniques.

Trust network is widely considered to be one of the most important aspects of social networks. It has many applications in the field of recommender systems and opinion formation. Few researchers have addressed the problem of trust/distrust prediction and, it has not yet been established whether the similarity measures can do trust prediction. The present paper aims to validate that similar users have related trust relationships. To predict trust relations between two users, the LookLike algorithm was introduced. Then we used the LookLike algorithm results as new features for supervised classifiers to predict the trust/distrust label. We chose a list of similarity measures to examined our claim on four real-world trust network datasets. The results demonstrated that there is a strong correlation between users' similarity and their opinion on trust networks. Due to the tight relation between trust prediction and truth discovery, we believe that our similarity-based algorithm could be a promising solution in their challenging domains.

Many social networks contain sensitive relational information. One approach to protect the sensitive relational information while offering flexibility for social network research and analysis is to release synthetic social networks at a pre-specified privacy risk level, given the original observed network. We propose the DP-ERGM procedure that synthesizes networks that satisfy the differential privacy (DP) via the exponential random graph model (EGRM). We apply DP-ERGM to a college student friendship network and compare its original network information preservation in the generated private networks with two other approaches: differentially private DyadWise Randomized Response (DWRR) and Sanitization of the Conditional probability of Edge given Attribute classes (SCEA). The results suggest that DP-EGRM preserves the original information significantly better than DWRR and SCEA in both network statistics and inferences from ERGMs and latent space models. In addition, DP-ERGM satisfies the node DP, a stronger notion of privacy than the edge DP that DWRR and SCEA satisfy.

Research on the design of data center infrastructure is increasing, both from academia and industry, due to the rapid development of cloud-based applications such as search engines, social networks, and large-scale computing. On a large scale, data centers can consist of hundreds to thousands of servers that require systems with high-performance requirements and low downtime. To meet the network's needs in a dynamic data center, infrastructure of applications and services are growing. It takes a process of designing a network topology so that it can guarantee availability and security. One way to surmount this is by implementing the zero trust security model based on micro-segmentation. Zero trust is a security idea based on the principle of "never trust, always verify" in which no concepts of trust and untrust in network traffic. The zero trust security model implemented network traffic in the form of untrust. Micro-segmentation is a way to achieve zero trust by dividing a network into smaller logical segments to restrict the traffic. In this research, data center network performance based on software-defined networking with zero trust security model using micro-segmentation has been evaluated using a testbed simulation of Cisco Application Centric Infrastructure by measuring the round trip time, jitter, and packet loss during experiments. Performance evaluation results show that micro-segmentation adds an average round trip time of 4 μs and jitter of 11 μs without packet loss so that the security can be improved without significantly affecting network performance on the data center.

OLAP is an authoritative analytical tool in the emerging big data analytics context, with particular regards to the target distributed environments (e.g., Clouds). Here, privacy-preserving OLAP-based big data analytics is a critical topic, with several amenities in the context of innovative big data application scenarios like smart cities, social networks, bio-informatics, and so forth. The goal is that of providing privacy preservation during OLAP analysis tasks, with particular emphasis on the privacy of OLAP aggregates. Following this line of research, in this paper we provide a deep contribution on experimenting and assessing a state-of-the-art distributed privacy-preserving OLAP framework, named as SPPOLAP, whose main benefit is that of introducing a completely-novel privacy notion for OLAP data cubes.

Recommended systems are very popular nowadays. They are used online to help a user get the desired product quickly. Recommended Systems are found on almost every website, especially big companies such as Facebook, eBay, Amazon, NetFlix, and others. In specific cases, these systems help the user find a book, movie, article, product of his or her preference, and are also used on social networks to meet friends who share similar interests in different fields. These companies use referral systems because they bring amazing benefits in a very fast time. To generate more accurate recommendations, recommended systems are based on the user's personal information, eg: different ratings, history observation, personal profiles, etc. Use of these systems is very necessary but the way this information is received, and the privacy of this information is almost constantly ignored. Many users are unaware of how their information is received and how it is used. This paper will discuss how recommended systems work in different online companies and how safe they are to use without compromising their privacy. Given the widespread use of these systems, an important issue has arisen regarding user privacy and security. Collecting personal information from recommended systems increases the risk of unwanted exposure to that information. As a result of this paper, the reader will be aware of the functioning of Recommended systems, the way they receive and use their information, and will also discuss privacy protection techniques against Recommended systems.

Nowadays, a massive number of data, referred as big data, are being collected from social networks and Internet of Things (IoT), which are of tremendous value. Many deep learning-based methods made great progress in the extraction of knowledge of those data. However, the knowledge extraction of the law data poses vast challenges on the deep learning, since the law data usually contain the privacy information. In addition, the amount of law data of an institution is not large enough to well train a deep model. To solve these challenges, some privacy-preserving deep learning are proposed to capture knowledge of privacy data. In this paper, we review the emerging topics of deep learning for the feature learning of the privacy data. Then, we discuss the problems and the future trend in deep learning for privacy-preserving feature learning on law data.

Social networks are highly preferred to express opinions, share information, and communicate with others on arbitrary topics. However, the downside is that many cybercriminals are leveraging social networks for cyber-crime. Internet Relay Chat (IRC) is the important social networks which can grant the anonymity to users by allowing them to connect channels without sign-up process. Therefore, IRC has been the playground of hackers and anonymous users for various operations such as hacking, cracking, and carding. Hence, it is urgent to study effective methods which can identify the authors behind the IRC messages. In this paper, we design an autonomic IRC monitoring system, performing recursive deep learning for classifying threat levels of messages and develop a novel author verification approach with one-class classification with deep autoencoder neural networks. The experimental results show that our approach can successfully perform effective author verification for IRC users.

The advances in natural language processing and the wide use of social networks have boosted the proliferation of chatbots. These are software services typically embedded within a social network, and which can be addressed using conversation through natural language. Many chatbots exist with different purposes, e.g., to book all kind of services, to automate software engineering tasks, or for customer support. In previous work, we proposed the use of chatbots for domain-specific modelling within social networks. In this short paper, we report on the needs for flexible modelling required by modelling using conversation. In particular, we propose a process of meta-model relaxation to make modelling more flexible, followed by correction steps to make the model conforming to its meta-model. The paper shows how this process is integrated within our conversational modelling framework, and illustrates the approach with an example.

Blockchain is the one of leading technology of this time; it has started to revolutionize several fields like, finance, business, industry, smart home, healthcare, social networks, Internet and the Internet of Things. It has many benefits like, decentralized network, robustness, availability, stability, anonymity, auditability and accountability. The applications of Blockchain are emerging, and it is found that most of the work is focused on its engineering implementation. While the theoretical part is very less considered and explored. In this paper we implemented the simulation of mining process in Blockchain based systems using queuing theory. We took the parameters of one of the mature Cryptocurrency, Bitcoin's real data and simulated using M/M/n/L queuing system in JSIMgraph. We have achieved realistic results; and expect that it will open up new research direction in theoretical research of Blockchain based systems.

Privacy preservation on social networks is nowadays a societal issue. In this paper, our contributions are to establish such a model for privacy preservation. We use differential privacy for personal privacy analysis and measurement. Our conclusion is that privacy could be measured and preserved if the corresponding approaches could be taken.

Privacy preservation on social networks is nowadays a societal issue. In this paper, our contributions are to establish such a model for privacy preservation. We use differential privacy for personal privacy analysis and measurement. Our conclusion is that privacy could be measured and preserved if the corresponding approaches could be taken.

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.

Influence Maximization is an important research content in the dissemination process of information and behavior in social networks. Because Hill Climbing and Greedy Algorithm have good dissemination effect on this topic, researchers have used it to solve this NP problem for a long time. These algorithms only consider the number of active nodes in each round, ignoring the characteristic that the influence will be accumulated, so its effect is still far from the optimal solution. Also, the time complexity of these algorithms is considerable. Aiming at the problem of Influence Maximization, this paper improves the traditional Hill Climbing and Greedy Algorithm. We propose a Hybrid Distribution Value Accumulation Algorithm for Influence Maximization, which has better activation effect than Hill Climbing and Greedy Algorithm. In the first stage of the algorithm, the region is numerically accumulating rapidly and is easy to activate through value-greed. Experiments are conducted on two data sets: the voting situation on Wikipedia and the transmission situation of Gnutella node-to-node file sharing network. Experimental results verify the efficiency of our methods.

Capturing the patterns in adversarial movement can present crucial insight into team dynamics and organization of cybercrimes. This information can be used for additional assessment and comparison of decision making approaches during cyberattacks. In this study, we propose a data-driven analysis based on time series analysis and social networks to identify patterns and alterations in time allocated to intrusion stages and adversarial movements. The results of this analysis on two case studies of collegiate cybersecurity exercises is provided as well as an analytical comparison of their behavioral trends and characteristics. This paper presents preliminary insight into complexities of individual and group level adversarial movement and decision-making as cyberattacks unfold.

From financial services platforms to social networks to vehicle control, software has come to mediate many activities of daily life. Governing bodies and standards organizations have responded to this trend by creating regulations and standards to address issues such as safety, security and privacy. In this environment, the compliance of software development to standards and regulations has emerged as a key requirement. Compliance claims and arguments are often captured in assurance cases, with linked evidence of compliance. Evidence can come from testcases, verification proofs, human judgement, or a combination of these. That is, we try to build (safety-critical) systems carefully according to well justified methods and articulate these justifications in an assurance case that is ultimately judged by a human. Yet software is deeply rooted in uncertainty making pragmatic assurance more inductive than deductive: most of complex open-world functionality is either not completely specifiable (due to uncertainty) or it is not cost-effective to do so, and deductive verification cannot happen without specification. Inductive assurance, achieved by sampling or testing, is easier but generalization from finite set of examples cannot be formally justified. And of course the recent popularity of constructing software via machine learning only worsens the problem - rather than being specified by predefined requirements, machine-learned components learn existing patterns from the available training data, and make predictions for unseen data when deployed. On the surface, this ability is extremely useful for hard-to specify concepts, e.g., the definition of a pedestrian in a pedestrian detection component of a vehicle. On the other, safety assessment and assurance of such components becomes very challenging. In this talk, I focus on two specific approaches to arguing about safety and security of software under uncertainty. The first one is a framework for managing uncertainty in assurance cases (for "conventional" and "machine-learned" systems) by systematically identifying, assessing and addressing it. The second is recent work on supporting development of requirements for machine-learned components in safety-critical domains.

Individuals may change their opinion in effect of a wide range of factors like interaction with peer groups, governmental policies and personal intentions. Works in this area mainly focus on individuals in social network and their interactions while neglect other factors. In this paper we have introduced an opinion formation model that consider the internal tendency as a personal feature of individuals in social network. In this model agents may trust, distrust or be neutral to their neighbors. They modify their opinion based on the opinion of their neighbors, trust/distrust to them while considering the internal tendency. The results of simulation show that this model can predict the opinion of social network especially when the average of nodal degree and clustering coefficient are high enough. Since this model can predict the preferences of individuals in market, it can be used to define marketing and production strategy.

The paper shows the influence of deactivated agents in social networks: switching between French-De Groot models and Friedkin-Johnsen model.

With the development of Online Social Networks(OSNs), OSNs have been becoming very popular platforms to publish resources and to establish relationship with friends. However, due to the lack of prior knowledge of others, there are usually risks associated with conducting network activities, especially those involving money. Therefore, it will be necessary to quantify the trust relationship of users in OSNs, which can help users decide whether they can trust another user. In this paper, we present a novel method for evaluating trust in OSNs using knowledge graph (KG), which is the cornerstone of artificial intelligence. And we focus on the two contributions for trust evaluation in OSNs: (i) a novel method using RNN to quantify trustworthiness in OSNs, which is inspired by relationship prediction in KG; (ii) a Path Reliability Measuring algorithm (PRM) to decide the reliability of a path from the trustor to the trustee. The experiment result shows that our method is more effective than traditional methods.

Spam emails have been a chronic issue in computer security. They are very costly economically and extremely dangerous for computers and networks. Despite of the emergence of social networks and other Internet based information exchange venues, dependence on email communication has increased over the years and this dependence has resulted in an urgent need to improve spam filters. Although many spam filters have been created to help prevent these spam emails from entering a user's inbox, there is a lack or research focusing on text modifications. Currently, Naive Bayes is one of the most popular methods of spam classification because of its simplicity and efficiency. Naive Bayes is also very accurate; however, it is unable to correctly classify emails when they contain leetspeak or diacritics. Thus, in this proposes, we implemented a novel algorithm for enhancing the accuracy of the Naive Bayes Spam Filter so that it can detect text modifications and correctly classify the email as spam or ham. Our Python algorithm combines semantic based, keyword based, and machine learning algorithms to increase the accuracy of Naive Bayes compared to Spamassassin by over two hundred percent. Additionally, we have discovered a relationship between the length of the email and the spam score, indicating that Bayesian Poisoning, a controversial topic, is actually a real phenomenon and utilized by spammers.

Personal privacy is an important issue when publishing social network data. An attacker may have information to reidentify private data. So, many researchers developed anonymization techniques, such as k-anonymity, k-isomorphism, l-diversity, etc. In this paper, we focus on graph k-degree anonymity by editing edges. Our method is divided into two steps. First, we propose an efficient algorithm to find a new degree sequence with theoretically minimal edit cost. Second, we insert and delete edges based on the new degree sequence to achieve k-degree anonymity.

Major online messaging services such as Facebook Messenger and WhatsApp are starting to provide users with real-time information about when people read their messages, while useful, the feature has the potential to negatively impact privacy as well as cause concern over access to self. We report on two surveys using Mechanical Turk which looked at senders' (N=402\vphantom\\ use of and reactions to the `message seen' feature, and recipients' (N=316) privacy and signaling behaviors in the face of such visibility. Our findings indicate that senders experience a range of emotions when their message is not read, or is read but not answered immediately. Recipients also engage in various signaling behaviors in the face of visibility by both replying or not replying immediately.

The following topics are dealt with: feature extraction; data mining; support vector machines; mobile computing; photovoltaic power systems; mean square error methods; fault diagnosis; natural language processing; control system synthesis; and Internet of Things.

Whether an idea, information, disease, or innovation diffuses throughout a society depends not only on the structure of the network of interactions, but also on the timing of those interactions. Recent studies have shown that diffusion can fail on a network in which people are only active in "bursts," active for a while and then silent for a while, but diffusion could succeed on the same network if people were active in a more random Poisson manner. Those studies generally consider models in which nodes are active according to the same random timing process and then ask which timing is optimal. In reality, people differ widely in their activity patterns – some are bursty and others are not. We model diffusion on networks in which agents differ in their activity patterns. We show that bursty behavior does not always hurt the diffusion, and in fact having some (but not all) of the population be bursty significantly helps diffusion. We prove that maximizing diffusion requires heterogeneous activity patterns across agents, and the overall maximizing pattern of agents' activity times does not involve any Poisson behavior.