Key allocation schemes for private social networks

We study the problem of limiting privacy loss due to data shared in a social network, where the basic underlying assumptions are that users are interested in sharing data and cannot be assumed to constantly follow appropriate privacy policies. Note that if these two assumptions do not hold, social network privacy is theoretically very easy to achieve; for instance, via some form of access control and confidentiality transformation on the data.In this paper we observe that users-regulated access control has shown to be unsuccessful for practical social network, and propose that social networks deploy an additional layer of server-assisted access control which, even under no action from a user, automatically evolves over time, by restricting access to the user’s data. The evolving access control mechanism provides non-trivial quantifiable guarantees for formally specified requirements of utility (i.e., users share as much data as possible to all other users) and privacy (i.e., users expose combinations of sensitive data only with low probability and over a long time). To the best of our knowledge, this is the first research solution attempting to simultaneously maximizes utility and safeguards privacy of users sharing data in social networking websites.KeywordsSocial NetworkingPrivacyUtilityAccess Control

An efficient key management scheme for content access control for linear hierarchies

Social Networks, Rule of Law and Economic Growth in China: The Elusive Pursuit of the Right Combination of Private and Public Ordering

Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this objective, we explore the impact of the attributes of sparse distribution on data utility. Firstly, we propose a new utility metric that emphasizes network structure distortion and attribute value loss. Furthermore, we design and implement a differentiated k-anonymity l-diversity social network anonymity algorithm, which seeks to protect users’ privacy in social networks and increase the usability of the published anonymized data. Its key idea is that it divides a node into two child nodes and only anonymizes sensitive values to satisfy anonymity requirements. The evaluation results show that our method can effectively improve the data utility as compared to generalized anonymizing algorithms.

With the rapid development of social networks, users pay more and more attention to the protection of personal information. However, the transmission of users’ personal information through social networks will inevitably lead to privacy leakage and make users attacked. A large amount of privacy information can be inferred from the content and social traces published by users, which leads to the rise of privacy inference technology for users in social networks. Social relationship inference and attribute inference are two basic attacks on users’ privacy in social networks. This is the first systematic review of privacy inference attacks in social networks. The purpose of this retrospective study is to provide a global summary, summarizing the time trend of the topic, and show the evolution of the topic in the past 15 years. In addition, this paper discusses the trend and development of this topic and finally looks forward to future work. The contribution of our work is helpful for researchers to continue their research in this field.

Recently, as more and more social network data has been published in one way or another, preserving privacy in publishing social network data becomes an important concern. With some local knowledge about individuals in a social network, an adversary may attack the privacy of some victims easily. Unfortunately, most of the previous studies on privacy preservation can deal with relational data only, and cannot be applied to social network data. In this paper, we take an initiative towards preserving privacy in social network data. We identify an essential type of privacy attacks: neighborhood attacks. If an adversary has some knowledge about the neighbors of a target victim and the relationship among the neighbors, the victim may be re-identified from a social network even if the victim's identity is preserved using the conventional anonymization techniques. We show that the problem is challenging, and present a practical solution to battle neighborhood attacks. The empirical study indicates that anonymized social networks generated by our method can still be used to answer aggregate network queries with high accuracy.

With the rapid development of the Internet, many users start to take action putting personal or company information on it, and share with everyone. The Internet is public as it were, if we do not control its limit of authority to assure security, it's possible that those attackers do illegal access of important information and destroy them. Not only personal privacy is invaded, but the mass property damage. Therefore, effective access control system has been more and more emphasized these days. To fight against these network attacks, it is necessary to establish an effective and safe access control system; here we proposed a scheme, a key management which called Lagrange interpolation mainly takes access control model as framework, and use Elliptic Curve Cryptography system to enhance security. The fact we choose Lagrange interpolation is the key we use is randomized, no relationship between each key, so is relatively hard. As Elliptic Curve Cryptography system, we want attackers to encounter Elliptic Curve Discrete Logarithm Problem. Once the prime number is big enough, attackers will have trouble deciphering the key. Access control is so comprehensive, such as electronic documents, online television system sand wireless networks and so on, from previous literature; mobile agent technology applied in access control and key management would waste space and do exist some flaws in security, also we still have a lot of works to do on medical application. Hence, we propose these schemes in mobile agent in order to reach improvement, and then analyze of security and try to simulate what attackers will do. We conclude four common attacks: External Collective Attack, Internal Attack, Collusion Attacks and Equation Breaking Attack. As results, attackers are hard to decipher the key because of no relationship between each key and will face Elliptic Curve Discrete Logarithm Problem. We confirm that the proposed schemes can be more efficiently and safety to protect mobile agent.

P2P networks have characteristics of decentralization, autonomy, and dynamicity. The security problems caused by these characteristics have seriously affected further development of P2P networks. The authors did research on CL-PKC key management schemes. (1) They propose a certificateless-based key distribution scheme with multiple trusted centers that fits the characteristics of P2P networks, and analyzed its security. (2) They also propose an improved interactive key agreement protocol across multiple domains, and then compare it with some existing key agreement protocol from aspects of security and computational efficiency. (3) The authors have implemented the proposed key management schemes, then verified their correctness and tested their computational efficiency. Combined with master key share management and key management of nodes, this system constructed a complete certificateless-based key management model, which is an exploration to solve security problems in P2P networks.

In order to prevent the disclosure of privacy-sensitive data, such as names and relations between users, social network graphs have to be anonymised before publication. Naive anonymisation of social network graphs often consists in deleting all identifying information of the users, while maintaining the original graph structure. Various types of attacks on naively anonymised graphs have been developed. Active attacks form a special type of such privacy attacks, in which the adversary enrols a number of fake users, often called sybils, to the social network, allowing the adversary to create unique structural patterns later used to re-identify the sybil nodes and other users after anonymisation. Several studies have shown that adding a small amount of noise to the published graph already suffices to mitigate such active attacks. Consequently, active attacks have been dubbed a negligible threat to privacy-preserving social graph publication. In this paper, we argue that these studies unveil shortcomings of specific attacks, rather than inherent problems of active attacks as a general strategy. In order to support this claim, we develop the notion of a robust active attack, which is an active attack that is resilient to small perturbations of the social network graph. We formulate the design of robust active attacks as an optimisation problem and we give definitions of robustness for different stages of the active attack strategy. Moreover, we introduce various heuristics to achieve these notions of robustness and experimentally show that the new robust attacks are considerably more resilient than the original ones, while remaining at the same level of feasibility.

We have conducted the first thorough analysis of the market for privacy practices and policies in online social networks. From an evaluation of 45 social networking sites using 260 criteria we find that many popular assumptions regarding privacy and social networking need to be revisited when considering the entire ecosystem instead of only a handful of well-known sites. Contrary to the common perception of an oligopolistic market, we find evidence of vigorous competition for new users. Despite observing many poor security practices, there is evidence that social network providers are making efforts to implement privacy enhancing technologies with substantial diversity in the amount of privacy control offered. However, privacy is rarely used as a selling point, even then only as auxiliary, nondecisive feature. Sites also failed to promote their existing privacy controls within the site. We similarly found great diversity in the length and content of formal privacy policies, but found an opposite promotional trend: though almost all policies are not accessible to ordinary users due to obfuscating legal jargon, they conspicuously vaunt the sites’ privacy practices. We conclude that the market for privacy in social networks is dysfunctional in that there is significant variation in sites’ privacy controls, data collection requirements, and legal privacy policies, but this is not effectively conveyed to users. Our empirical findings motivate us to introduce the novel model of a privacy communication game, where the economically rational choice for a site operator is to make privacy control available to evade criticism from privacy fundamentalists, while hiding the privacy control interface and privacy policy to maximize sign-up numbers and encourage data sharing from the pragmatic majority of users.KeywordsSocial NetworkPrivacy PolicyPrivacy PracticeData ProtectionSocial Networking SiteThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Social media plays a vital role in the user community all over the globe. It makes it easy to communicate from one person to another person through these social media platforms. But these platforms are coming under various security issues and privacy of user-related data that make it tough to maintain. Test data generated from existing tools are used for analysis on these platforms. According to various roles in any real-world application, environment for the user, such as community deduction, analyzing user profiles, and preventing security threats, is performed on these data. In this paper, we have surveyed various factors related to security and privacy in a social network and listed various advantages and disadvantages of various approaches, thereby it acts as a base paper for future research work in the field of social networking.KeywordsSocial mediaSecurityPrivacy

In humanware applications, resource constrained sensor devices are tactically placed on human body to form Wireless Body Area Networks (WBAN). Then the WBAN is used for monitoring biometrics and movements of human body. Keeping in mind the resource constrained devices, WBANs are treated just like wireless sensor networks when considering a solution for key management. However, WBANs differ from traditional wireless sensor networks in scale, topology and security requirements. Also, the WBANs can also use random values from biometric measurements for the generation of keys. These differences render the key management schemes of WSNs inefficient and overly complex for WBANs scenario. Therefore, we propose a key management scheme that is efficient and fulfills the security requirements of WBAN.

A Smart grid is a modern electricity delivery system. It is an integration of energy systems and other necessary elements including traditional upgrades and new grid technologies with renewable generation and increased consumer storage. It uses information and communication technology (ICT) to operate, monitor and control data between the generation source and the end user. Smart grids have duplex power flow and communication to achieve high efficiency, reliability, environmental, economics, security and safety standards. However, along with unique facilities, smart grids face security challenges such as access control, connectivity, fault tolerance, privacy, and other security issues. Cyber-attacks, in the recent past, on critical infrastructure including smart grids have highlighted security as a major requirement for smart grids. Therefore, cryptography and key management are necessary for smart grids to become secure and realizable. Key management schemes are processes of key organizational frameworks, distribution, generation, refresh and key storage policies. Currently, several secure schemes, related to key management for smart grid have been proposed to achieve end-to-end secure communication. This paper presents a comprehensive survey and discussion on the current state of the key management of smart grids.

Numerous key management schemes have been proposed for sensor networks. The objective of key management is to dynamically establish and maintain secure channels among communicating nodes. Desired features of key management in sensor networks include energy awareness, localized impact of attacks, and scaling to a large number of nodes. A primary challenge is managing the trade-off between providing acceptable levels of security and conserving scarce resources, in particular energy, needed for network operations. Many schemes, referred to as static schemes, have adopted the principle of key predistribution with the underlying assumption of a relatively static short-lived network (node replenishments are rare, and keys outlive the network). An emerging class of schemes, dynamic key management schemes, assumes long-lived networks with more frequent addition of new nodes, thus requiring network rekeying for sustained security and survivability. In this article we present a classification of key management schemes in sensor networks delineating their similarities and differences. We also describe a novel dynamic key management scheme, localized combinatorial keying (LOCK), and compare its security and performance with a representative static key management scheme. Finally, we outline future research directions.

Existing works on identity privacy protection on social networks make the assumption that all the user identities in a social network are private and ignore the fact that in many real-world social networks, there exists a considerable amount of users such as celebrities, media users, and organization users whose identities are public. In this paper, we demonstrate that the presence of public users can cause serious damage to the identity privacy of other ordinary users. Motivated attackers can utilize the connection information of a user to some known public users to perform re-identification attacks, namely connection fingerprint (CFP) attacks. We propose two k-anonymization algorithms to protect a social network against the CFP attacks. One algorithm is based on adding dummy vertices. It can resist powerful attackers with the connection information of a user with the public users within n hops (n ≥ 1) and protect the centrality utility of public users. The other algorithm is based on edge modification. It is only able to resist attackers with the connection information of a user with the public users within 1 hop but preserves a rich spectrum of network utility. We perform comprehensive experiments on real-world networks and demonstrate that our algorithms are very efficient in terms of the running time and are able to generate k-anonymized networks with good utility.