Online Privacy as a Collective Phenomenon

The problem of online privacy is often reduced to individual decisions to hide or reveal personal information in online social networks (OSNs). However, with the increasing use of OSNs, it becomes more important to understand the role of the social network in disclosing personal information that a user has not revealed voluntarily: How much of our private information do our friends disclose about us, and how much of our privacy is lost simply because of online social interaction? Without strong technical effort, an OSN may be able to exploit the assortativity of human private features, this way constructing shadow profiles with information that users chose not to share. Furthermore, because many users share their phone and email contact lists, this allows an OSN to create full shadow profiles for people who do not even have an account for this OSN. We empirically test the feasibility of constructing shadow profiles of sexual orientation for users and non-users, using data from more than 3 Million accounts of a single OSN. We quantify a lower bound for the predictive power derived from the social network of a user, to demonstrate how the predictability of sexual orientation increases with the size of this network and the tendency to share personal information. This allows us to define a privacy leak factor that links individual privacy loss with the decision of other individuals to disclose information. Our statistical analysis reveals that some individuals are at a higher risk of privacy loss, as prediction accuracy increases for users with a larger and more homogeneous first- and second-order neighborhood of their social network. While we do not provide evidence that shadow profiles exist at all, our results show that disclosing of private information is not restricted to an individual choice, but becomes a collective decision that has implications for policy and privacy regulation

Why Do Cascade Sizes Follow a Power-Law?

We introduce random directed acyclic graph and use it to model the information diffusion network. Subsequently, we analyze the cascade generation model (CGM) introduced by Leskovec et al. [19]. Until now only empirical studies of this model were done. In this paper, we present the first theoretical proof that the sizes of cascades generated by the CGM follow the power-law distribution, which is consistent with multiple empirical analysis of the large social networks. We compared the assumptions of our model with the Twitter social network and tested the goodness of approximation.Comment: 8 pages, 7 figures, accepted to WWW 201

Feature-rich networks: going beyond complex network topologies.

Abstract The growing availability of multirelational data gives rise to an opportunity for novel characterization of complex real-world relations, supporting the proliferation of diverse network models such as Attributed Graphs, Heterogeneous Networks, Multilayer Networks, Temporal Networks, Location-aware Networks, Knowledge Networks, Probabilistic Networks, and many other task-driven and data-driven models. In this paper, we propose an overview of these models and their main applications, described under the common denomination of Feature-rich Networks, i. e. models where the expressive power of the network topology is enhanced by exposing one or more peculiar features. The aim is also to sketch a scenario that can inspire the design of novel feature-rich network models, which in turn can support innovative methods able to exploit the full potential of mining complex network structures in domain-specific applications

XYZ Privacy

Future autonomous vehicles will generate, collect, aggregate and consume significant volumes of data as key gateway devices in emerging Internet of Things scenarios. While vehicles are widely accepted as one of the most challenging mobility contexts in which to achieve effective data communications, less attention has been paid to the privacy of data emerging from these vehicles. The quality and usability of such privatized data will lie at the heart of future safe and efficient transportation solutions. In this paper, we present the XYZ Privacy mechanism. XYZ Privacy is to our knowledge the first such mechanism that enables data creators to submit multiple contradictory responses to a query, whilst preserving utility measured as the absolute error from the actual original data. The functionalities are achieved in both a scalable and secure fashion. For instance, individual location data can be obfuscated while preserving utility, thereby enabling the scheme to transparently integrate with existing systems (e.g. Waze). A new cryptographic primitive Function Secret Sharing is used to achieve non-attributable writes and we show an order of magnitude improvement from the default implementation.Comment: arXiv admin note: text overlap with arXiv:1708.0188

The good, the bad and their kins: identifying questions with negative scores in StackOverflow

A rapid increase in the number of questions posted on community question answering (CQA) forums is creating a need for automated methods of question quality moderation to improve the effectiveness of such forums in terms of response time and quality. Such automated approaches should aim to classify questions as good or bad for a particular forum as soon as they are posted based on the guidelines and quality standards defined/listed by the forum. Thus, if a question meets the standard of the forum then it is classified as good else we classify it as bad. In this paper, we propose a method to address this problem of question classification by retrieving similar questions previously asked in the same forum, and then using the text from these previously asked similar questions to predict the quality of the current question. We empirically validate our proposed approach on the set of StackOverflow data, a massive CQA forum for programmers, comprising of about 8M questions. With the use of these additional text retrieved from similar questions, we are able to improve the question quality prediction accuracy by about 2.8% and improve the recall of negatively scored questions by about 4.2%. This improvement of 4.2% in recall would be helpful in automatically flagging questions as bad (unsuitable) for the forum and will speed up the moderation process thus saving time and human effort

Scaling up Group Closeness Maximization

Closeness is a widely-used centrality measure in social network analysis. For a node it indicates the inverse average shortest-path distance to the other nodes of the network. While the identification of the k nodes with highest closeness received significant attention, many applications are actually interested in finding a group of nodes that is central as a whole. For this problem, only recently a greedy algorithm with approximation ratio (1−1/e) has been proposed [Chen et al., ADC 2016]. Since this algorithm’s running time is still expensive for large networks, a heuristic without approximation guarantee has also been proposed in the same paper. In the present paper we develop new techniques to speed up the greedy algorithm without losing its theoretical guarantee. Compared to a straightforward implementation, our approach is orders of magnitude faster and, compared to the heuristic proposed by Chen et al., we always find a solution with better quality in a comparable running time in our experiments. Our method Greedy++ allows us to approximate the group with maximum closeness on networks with up to hundreds of millions of edges in minutes or at most a few hours. To have the same theoretical guarantee, the greedy approach by [Chen et al., ADC 2016] would take several days already on networks with hundreds of thousands of edges. In a comparison with the optimum, our experiments show that the solution found by Greedy++ is actually much better than the theoretical guarantee. Over all tested networks, the empirical approximation ratio is never lower than 0.97. Finally, we study for the first time the correlation between the top-k nodes with highest closeness and an approximation of the most central group in large complex networks and show that the overlap between the two is relatively small

Computing Top-k Closeness Centrality Faster in Unweighted Graphs. (Technical Report)

Centrality indices are widely used analytic measures for the importance of nodes in a network. Closeness centrality is very popular among these measures. For a single node v, it takes the sum of the distances of v to all other nodes into account. The currently best algorithms in practical applications for computing the closeness for all nodes exactly in unweighted graphs are based on breadth-first search (BFS) from every node. Thus, even for sparse graphs, these algorithms require quadratic running time in the worst case, which is prohibitive for large networks. In many relevant applications, however, it is unnecessary to compute closeness values for all nodes. Instead, one requires only the k nodes with the highest closeness values in descending order. Thus, we present a new algorithm for computing this top-k ranking in unweighted graphs. Following the rationale of previous work, our algorithm significantly reduces the number of traversed edges. It does so by computing upper bounds on the closeness and stopping the current BFS search when k nodes already have higher closeness than the bounds computed for the other nodes. In our experiments with real-world and synthetic instances of various types, one of these new bounds is good for small-world graphs with low diameter (such as social networks), while the other one excels for graphs with high diameter (such as road networks). Combining them yields an algorithm that is faster than the state of the art for top-k computations for all test instances, by a wide margin for high-diameter graphs