The Social Psychology of Groups. J. W Thibaut & H. H. Kelley. New York: alley, 1959. The team of Thibaut and Kelley goes back to 1946 when, after serving in different units of the armed services psychology program, the authors joined the Research Center for Group Dynamics, first at M.LT and then at the University of Michigan. Their continued association eventuated in appointments as fellows at the Center for Advanced Study in the Behavioral Sciences, 19561957. It is during these years that their collaboration resulted in the publication of The Social Psychology of Groups. The book was designed to "bring order and coherence to present-day research in interpersonal relations and group functioning." To accomplish this aim, the authors introduced, defined, and illustrated basic concepts in an effort to explain the simplest of social phenomena, the two-person relationship. These basic principles and concepts were then employed to illuminate larger problems and more complex social relationships and to examine the significance of such concepts as roles, norm, power, group cohesiveness, and status. The lasting legacy of this book is derived from the fact that the concepts and principles discussed therein serve as a foundation for one of the dominant conceptual frameworks in the field of family studies today-the social exchange framework. Specifically, much of our contemporary thinking about the process of interpersonal attraction and about how individuals evaluate their close relationships has been influenced by the theory and concepts introduced in The Social Psychology of Groups. Today, as a result of Thibaut and Kelley, we think of interpersonal attraction as resulting from the unique valence of driving and restraining forces, rewards and costs, subjectively thought to be available from a specific relationship and its competing alternatives. We understand, as well, that relationships are evaluated through complex and subjectively based comparative processes. As a result, when we think about assessing the degree to which individuals are satisfied with their relationships, we take into consideration the fact that individuals differ in terms of the importance they attribute to different aspects of a relationship (e.g., financial security, sexual fulfillment, companionship). We also take into consideration the fact that individuals differ in terms of the levels of rewards and costs that they believe are realistically obtainable and deserved from a relationship. In addition, as a result of Thibaut and Kelley's theoretical focus on the concept of dependence and the interrelationship between attraction and dependence, there has evolved within the field of family studies a deeper appreciation for the complexities and variability found within relationships. Individuals are dependent on their relationships, according to Thibaut and Kelley, when the outcomes derived from the existing relationship exceed those perceived to be available in competing alternatives. Individuals who are highly dependent on their relationships are less likely to act to end their relationships. This dependence and the stability it engenders may or may not be voluntary, depending on the degree to which individuals are attracted to and satisfied with their relationships. When individuals are both attracted to and dependent on their relationships, they can be thought of as voluntarily participating in their relationship. That is, they are likely to commit themselves to the partner and relationship and actively work for its continuance. Thibaut and Kelley termed those relationships characterized by low levels of satisfaction and high levels of dependence "nonvoluntary relationships. …

The Social Psychology of Groups

Multiagent Systems is the title of a collection of papers dedicated to surveying specific themes of Multiagent Systems (MAS) and Distributed Artificial Intelligence (DAI). All of them authored by leading researchers of this dynamic multidisciplinary field.

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Multiagent systems: a modern approach to distributed artificial intelligence

Privacy-preserving data publishing is an important problem that has been the focus of extensive study. The state-of-the-art solution for this problem is differential privacy, which offers a strong degree of privacy protection without making restrictive assumptions about the adversary. Existing techniques using differential privacy, however, cannot effectively handle the publication of high-dimensional data. In particular, when the input dataset contains a large number of attributes, existing methods require injecting a prohibitive amount of noise compared to the signal in the data, which renders the published data next to useless.To address the deficiency of the existing methods, this paper presents PrivBayes, a differentially private method for releasing high-dimensional data. Given a dataset D, PrivBayes first constructs a Bayesian network N, which (i) provides a succinct model of the correlations among the attributes in D and (ii) allows us to approximate the distribution of data in D using a set P of low-dimensional marginals of D. After that, PrivBayes injects noise into each marginal in P to ensure differential privacy and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in D. Finally, PrivBayes samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data. Intuitively, PrivBayes circumvents the curse of dimensionality, as it injects noise into the low-dimensional marginals in P instead of the high-dimensional dataset D. Private construction of Bayesian networks turns out to be significantly challenging, and we introduce a novel approach that uses a surrogate function for mutual information to build the model more accurately. We experimentally evaluate PrivBayes on real data and demonstrate that it significantly outperforms existing solutions in terms of accuracy.

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PrivBayes: private data release via bayesian networks

Connections: An Introduction to the Economics of Networks by Sanjeev Goyal .

Modern cyber physical systems (CPSs) has widely being used in our daily lives because of development of information and communication technologies (ICT). With the provision of CPSs, the security and privacy threats associated to these systems are also increasing. Passive attacks are being used by intruders to get access to private information of CPSs. In order to make CPSs data more secure, certain privacy preservation strategies such as encryption, and k-anonymity have been presented in the past. However, with the advances in CPSs architecture, these techniques also need certain modifications. Meanwhile, differential privacy emerged as an efficient technique to protect CPSs data privacy. In this paper, we present a comprehensive survey of differential privacy techniques for CPSs. In particular, we survey the application and implementation of differential privacy in four major applications of CPSs named as energy systems, transportation systems, healthcare and medical systems, and industrial Internet of things (IIoT). Furthermore, we present open issues, challenges, and future research direction for differential privacy techniques for CPSs. This survey can serve as basis for the development of modern differential privacy techniques to address various problems and data privacy scenarios of CPSs.

Differential Privacy Techniques for Cyber Physical Systems: A Survey

Releasing high-dimensional data enables a wide spectrum of data mining tasks. Yet, individual privacy has been a major obstacle to data sharing. In this paper, we consider the problem of releasing high-dimensional data with differential privacy guarantees. We propose a novel solution to preserve the joint distribution of a high-dimensional dataset. We first develop a robust sampling-based framework to systematically explore the dependencies among all attributes and subsequently build a dependency graph. This framework is coupled with a generic threshold mechanism to significantly improve accuracy. We then identify a set of marginal tables from the dependency graph to approximate the joint distribution based on the solid inference foundation of the junction tree algorithm while minimizing the resultant error. We prove that selecting the optimal marginals with the goal of minimizing error is NP-hard and, thus, design an approximation algorithm using an integer programming relaxation and the constrained concave-convex procedure. Extensive experiments on real datasets demonstrate that our solution substantially outperforms the state-of-the-art competitors.

Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

Information networks, such as social media and email networks, often contain sensitive information. Releasing such network data could seriously jeopardize individual privacy. Therefore, we need to sanitize network data before the release. In this paper, we present a novel data sanitization solution that infers a network's structure in a differentially private manner. We observe that, by estimating the connection probabilities between vertices instead of considering the observed edges directly, the noise scale enforced by differential privacy can be greatly reduced. Our proposed method infers the network structure by using a statistical hierarchical random graph (HRG) model. The guarantee of differential privacy is achieved by sampling possible HRG structures in the model space via Markov chain Monte Carlo (MCMC). We theoretically prove that the sensitivity of such inference is only O(log n), where n is the number of vertices in a network. This bound implies less noise to be injected than those of existing works. We experimentally evaluate our approach on four real-life network datasets and show that our solution effectively preserves essential network structural properties like degree distribution, shortest path length distribution and influential nodes.

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Differentially private network data release via structural inference

Regularly releasing the aggregate statistics about data streams in a privacy-preserving way not only serves valuable commercial and social purposes, but also protects the privacy of individuals. This problem has already been studied under differential privacy, but only for the case of a single continuous query that covers the entire time span, e.g., counting the number of tuples seen so far in the stream. However, most real-world applications are window-based, that is, they are interested in the statistical information about streaming data within a window, instead of the whole unbound stream. Furthermore, a Data Stream Management System (DSMS) may need to answer numerous correlated aggregated queries simultaneously, rather than a single one. To cope with these requirements, we study how to release differentially private answers for a set of sliding window aggregate queries. We propose two solutions, each consisting of query sampling and composition. We first selectively sample a subset of representative sliding window queries from the set of all the submitted ones. The representative queries are answered by adding Laplace noises in a way satisfying differential privacy. For each non-representative query, we compose its answer from the query results of those representatives. The experimental evaluation shows that our solutions are efficient and effective.

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Efficient and accurate strategies for differentially-private sliding window queries

Recent studies show that insider attacks that aim at exfiltrating data are very common and that these attacks are performed according to specific patterns. Protecting against such threats requires complementing existing security techniques, such as access control and encryption, with tools able to detect anomalies in data accesses. In this paper, we present a technique specifically tailored for detecting anomalous database accesses. Our technique extracts users' access patterns based on both the syntax of the input queries and the amount of data in their output results. Our technique is based on mining SQL queries in database audit logs in order to form profiles of the normal users' access patterns. New queries are checked upon these profiles, and deviations from these profiles are considered anomalous and thus indicative of possible attempts to exfiltrate or misuse the data. Our technique works under two application scenarios. The first is when the database has role-based access control RBAC in place. Under an RBAC system, users belong to roles and privileges are associated with roles rather than individual users. For this scenario, we form profiles of roles which make our approach usable for database management systems DBMSs that have a large user population; in this scenario, we apply the naive Bayesian classifier which shows accurate results in practice. We also employ multilabeling classification to enhance accuracy when the access patterns are common to multiple roles. The second application scenario is when the DBMS does not apply RBAC. In this scenario, we apply the COBWEB clustering method. Experimental results indicate that our techniques are very effective. WIREs Data Mining Knowl Discov 2016, 6:231-239. doi: 10.1002/widm.1195

Data and syntax centric anomaly detection for relational databases

Todays online social networks (OSNs) allow a user to share his photos with others and tag the co-owners, i.e., friends who also appear in the co-owned photos. However, it is not uncommon that conflicts may arise among the co-owners because of their different privacy concerns. OSNs, unfortunately, offer only limited access control support where the publisher of the shared content is the sole decision maker to restrict access. There is thus an urgent need to develop mechanisms for multiple owners of the shared content to collaboratively determine the access rights of other users, as well as to resolve the conflicts among co-owners with different requirements. Rather than competing with each other and just wanting ones own decision to be executed, OSN users may be affected their peers concerns and adjust their decisions accordingly. To incorporate such peer effects in the strategy, we formulate a model to simulate an emotional mediation among multiple co-owners. Our mechanism, called CAPE, considers the intensity with which the co-owners are willing to pick up a choice (e.g. to release a photo to the public) and the extent to which they want their decisions to be affected by their peers actions. Moreover, CAPE automatically yields the final actions for the co-owners as the mediation reaches equilibrium. It frees the co-owners from the mediation process after the initial setting, and meanwhile, offers a way to achieve more agreements among themselves.

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Qian Xiao

Papers

Differentially Private High-Dimensional Data Publication via Sampling-Based Inference

Differentially private network data release via structural inference

Efficient and accurate strategies for differentially-private sliding window queries

Data and syntax centric anomaly detection for relational databases

Peer-aware collaborative access control in social networks