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The framework of differential privacy protects an individual’s privacy while publishing query responses on congregated data. In this work, a new noise addition mechanism for differential privacy is introduced where the noise added is sampled from a hybrid density that resembles Laplace in the centre and Gaussian in the tail. With a sharper centre and light, sub-Gaussian tail, this density has the best characteristics of both distributions. We theoretically analyze the proposed mechanism, and we derive the necessary and sufficient condition in one dimension and a sufficient condition in high dimensions for the mechanism to guarantee $(\epsilon,\delta)$ -differential privacy. Numerical simulations corroborate the efficacy of the proposed mechanism compared to other existing mechanisms in achieving a better trade-off between privacy and accuracy. [ABSTRACT FROM AUTHOR]

Triangle count is a critical parameter in mining relationships among people in social networks. However, directly publishing the findings obtained from triangle counts may bring potential privacy concern, which raises great challenges and opportunities for privacy-preserving triangle counting. In this paper, we choose to use differential privacy to protect triangle counting for large scale graphs. To reduce the large sensitivity caused in large graphs, we propose a novel graph projection method that can be used to obtain an upper bound for sensitivity in different distributions. In particular, we publish the triangle counts satisfying the node-differential privacy with two kinds of histograms: the triangle count distribution and the cumulative distribution. Moreover, we extend the research on privacy preserving triangle counting to one of its applications, the local clustering coefficient. Experimental results show that the cumulative distribution can fit the real statistical information better, and our proposed mechanism has achieved better accuracy for triangle counts while maintaining the requirement of differential privacy. [ABSTRACT FROM AUTHOR]

Vast data and computing resources are commonly needed to train deep neural networks, causing an unaffordable price for individual users. Motivated by the increasing demands of deep learning applications, sharing well-trained models becomes popular. The owner of a pre-trained model can share it by publishing the model directly or providing a prediction interface. Either way, individual users can benefit from deep learning without much cost, and computing resources can be saved. However, recent studies of machine learning security have identified severe threats to these model publishing approaches. This article will focus on the privacy leakage issue of publishing well-trained deep neural network models. To tackle this problem, we propose a series of secure model publishing solutions based on training task parallelism. Specifically, we show how to estimate private model parameters through parallel model training and generate new model parameters in a privacy-preserving manner to replace the original ones for publishing. Based on data parallelism and parameter generating techniques, we design another two solutions concentrating on model quality and parameter privacy, respectively. Through privacy leakage analysis and experimental attack evaluation, we conclude that deep neural network models published with our solutions can provide on-demand model quality guarantees and resist membership inference attacks. [ABSTRACT FROM AUTHOR]

Two main privacy models for sanitising datasets are differential privacy (DP) and syntactic privacy. The former restricts individual values’ impact on the output based on the dataset while the latter restructures the dataset before publication to link any record to multiple sensitive data values. Besides both providing mechanisms to sanitise data, these models are often applied independently of each other and very little is known regarding how they relate. Knowing how privacy models are related can help us develop a deeper understanding of privacy and can inform how a single privacy mechanism can fulfil multiple privacy models. In this paper, we introduce a framework that determines if the privacy mechanisms of one privacy model can also guarantee privacy for another privacy model. We apply our framework to understand the relationship between DP and a form of syntactic privacy called $t$ -closeness. We demonstrate, for the first time, how DP and $t$ -closeness can be interpreted in terms of each other by introducing generalisations and extensions of both models to explain the transition from one model to the other. Finally, we show how applying one mechanism to guarantee multiple privacy models increases data utility compared to applying separate mechanisms for each privacy model. [ABSTRACT FROM AUTHOR]

Reprint is a fast and efficient way for news media to spread information and plays an increasingly important role in evaluating the influence of media and building a brand image. News reprint prediction is a novel research question in the news diffusion field, which predicts whether a news media will reprint a piece of news in the future. During reprinting, an association network among media and entities in the news is formed that reflects their multidimensional dynamic interaction. Existing research primarily focuses on integrating reprint historical records and news content while reprint prediction considering the associations among media and entities in the news remains under-researched. This work develops an entity association network-enhanced method for news reprint prediction, which adopts HIN2Vec to model the dynamic interaction and incorporates the learned embedding and news content through attention mechanism to generate entity-specific content representation. The efficacy of the proposed method is validated on real-world news reprint data. Experimental results show that the fusion of entity association network helps improve the performance of reprint prediction. This research contributes to media communication literature and has significant practical implications. [ABSTRACT FROM AUTHOR]

Over the past decade, we have witnessed unprecedented development in deep learning (DL) and its contributions to modern networking systems. Along with its wide adoption, however, are growing concerns over the broad attack surfaces toward learning systems and the intrinsic vulnerabilities on privacy, security, robustness, and more. As a countermeasure to mitigate the threats or formalize a better defense, a widely adopted approach is to introduce a certain level of random perturbation (a.k.a. calibrated artificial noise) at either the training or prediction phase. Noteworthy examples include effective defenses against model inference attacks and notions of certified robustness. As such, differential privacy (DP), originally established as a privacy-preserving framework for data publishing, has drawn great interest from the learning community. Given a target utility and the acceptable trade-off, DP's formalization on the amount of noise needed has been shown to be widely applicable to a broad range of DL vulnerability mitigations. In this article, we present to our readers the recent representative advancements intersecting DL and DP, ranging from privacy enhancements for DL systems to security and robustness improvements and other novel extensions. Furthermore, we discuss the ongoing challenges and propose a number of future directions where DP has great potential to positively contribute to future DL systems. [ABSTRACT FROM AUTHOR]

Presents the list of reviewers who contributed to this publication in 2021. [ABSTRACT FROM AUTHOR]

Presents a listing of reviewers who contributed to this publication in 2021. [ABSTRACT FROM AUTHOR]

This letter develops POPCache (POpularity-aware Push-based Cache), a cache management strategy to support cache consistency in ICN-based IoT. Based on the proposed push mechanism, POPCache can proactively update the slated content to impose strong consistency while achieving low overhead, because POPCache relieves the heavy burden of nodes (which can be resource-constrained IoT devices) of maintaining extra information. POPCache makes caching decisions by jointly considering several determining factors. We use the theoretical study to analyze POPCache’s overhead. Moreover, we conduct simulations to compare POPCache with existing works; results show that POPCache improves cache consistency and achieves good cache and network performance. [ABSTRACT FROM AUTHOR]

During the early history of microprocessors, benchmarks were of interest to customers and important to marketing, but results and methods were not comparable. For example, a 1985 Performance SummaryaaThe 1985 Performance Summary is the third edition of a glossy, typeset, well-organized document with 164 pages and many tables and graphs. It includes work by multiple performance groups at a now-defunct computer manufacturer. The full title is not provided here because it is labeled " For Internal Use Only," although one suspects that customers may have routinely seen copies or excerpts. from a vendor of popular minicomputers contains results from a variety of benchmarks, with a variety of weaknesses as follows. [ABSTRACT FROM AUTHOR]

As more and more organizations deploy their sensing devices in the industrial Internet of Things network, it becomes increasingly important for the organizations to share data with others, so that the value of the data can be fully explored. However, individuals’ privacy may be compromised because of data sharing. In this article, we study the differentially private data publishing problem, which aims at balancing between privacy and data utility. Specifically, two blockchain-based data publishing protocols are proposed. For histogram publishing, we propose a protocol where the Laplace noise added in the query result is verified by the blockchain. For anonymized data publishing, we propose a protocol, which can prevent the publisher and the recipient from lying about the utility of the published data. With the blockchain acting as a reliable intermediary between the publisher and the recipient, the proposed protocols can help to realize fair and auditable data sharing. [ABSTRACT FROM AUTHOR]

Crowdsensing enables extensive data collection for Location-Based Services (LBS). However, there is a large amount of sensitive private information within the sensing data of the user’s location. Hence, it will pose a huge threat to user’s privacy if the sensing locations are published without protection. As usual, the user’s location has two typical attributes, i.e., a geographic one and a social semantic one. Hence, the secure publishing of the user’s sensing location data should consider how to protect both of the attributes-related privacy security. However, even for the same location, the same user may have different privacy protection needs along with different sensitive events, let alone that different users have different privacy protection needs due to their differences in age, gender, occupation, etc. Therefore, we propose a personalized secure publishing mechanism of the sensing location data. At first, it makes collaborative protection between semantic and geographic attribute-related privacy from two aspects, i.e., adjusting the user’s location information loss to meet different privacy protection needs of users, and personalizing the privacy-attack tolerance degree to achieve different privacy protection levels. Then, it optimizes the collaborative protection effects with the Stackelberg game to achieve the best balance between user data security and data quality. The experiments based on the real-world dataset demonstrate the effectiveness of our proposed mechanism. [ABSTRACT FROM AUTHOR]

Log data from mobile devices generally contain a series of events with temporal information including time intervals which consist of the start and finish times. However, the problem of releasing differentially private time interval datasets has not been tackled yet. A time interval dataset can be represented by a two dimensional (2D) histogram. Most of the methods to publish 2D histograms partition the data into rectangular spaces to reduce the aggregated noise error for range queries. However, the existing algorithms to publish 2D histograms suffer from the structural error when applied to time interval datasets. To reduce the aggregated noise errors and suppress the increase in the structural error, we propose the TIDY (publishing Time Intervals via Differential privacY) algorithm. We use the frequency vectors as a compact representation of the time interval dataset. After applying the Laplace mechanism to the frequency vectors, we improve the utility of the frequency vectors based on a maximum likelihood estimation. We also develop a new partitioning method adapted for the frequency vectors to balance the trade-off between the noise and structural errors. Our empirical study on real-life and synthetic datasets confirms that TIDY outperforms the existing algorithms for 2D histograms. [ABSTRACT FROM AUTHOR]

Presents a listing of reviewers who contributed to this publication in 2021. [ABSTRACT FROM AUTHOR]

Presents a summary of articles published in Computer 50 and 25 years ago. [ABSTRACT FROM AUTHOR]

The need to efficiently store and query large scale graph datasets is evident in the growing number of data-intensive applications, particularly to maximize the mining of intelligence from these data (e.g., to inform decision making). However, directly releasing graph dataset for analysis may leak sensitive information of an individual even if the graph is anonymized, as demonstrated by the re-identification attacks on the DBpedia datasets. A key challenge in the design of graph sanitization methods is scalability, as existing execution models generally have significant memory requirements. In this paper, we propose a novel k-decomposition algorithm and define a new information loss matrix designed for utility measurement in massively large graph datasets. We also propose a novel privacy preserving framework that can be seamlessly integrated with graph storage, anonymization, query processing, and analysis. Our experimental studies show that the proposed solution achieves privacy-preserving, utility, and efficiency. [ABSTRACT FROM AUTHOR]

The American National Academies of Sciences, Engineering, and Medicine (NASEM) has recently released the report "Reproducibility and Replicability in Science." The report has prompted discussions within many disciplines about the extent of the current adoption of reproducibility and replicability, the challenges involved in publishing reproducible results as well as strategies for improving. We organized a panel at the IEEE VIS conference 2019 to start a discussion on the reproducibility challenges faced by the visualization community and how those challenges might be addressed. In this viewpoint, we summarize key findings of the NASEM report, the panel discussion, and outline a set of recommendations for the visualization community. [ABSTRACT FROM AUTHOR]

In this paper, we study the problem of publishing high-dimensional data in a distributed multi-party environment under differential privacy. In particular, with the assistance of a semi-trusted curator, the parties (i.e., local data owners) collectively generate a synthetic integrated dataset while satisfying $\varepsilon$ ɛ -differential privacy. To solve this problem, we present a differentially private sequential update of Bayesian network (DP-SUBN) approach. In DP-SUBN, the parties and the curator collaboratively identify the Bayesian network $\mathbb {N}$ N that best fits the integrated dataset in a sequential manner, from which a synthetic dataset can then be generated. The fundamental advantage of adopting the sequential update manner is that the parties can treat the intermediate results provided by previous parties as their prior knowledge to direct how to learn $\mathbb {N}$ N . The core of DP-SUBN is the construction of the search frontier, which can be seen as a priori knowledge to guide the parties to update $\mathbb {N}$ N . By exploiting the correlations of attribute pairs, we propose exact and heuristic methods to construct the search frontier. In particular, to privately quantify the correlations of attribute pairs without introducing too much noise, we first put forward a non-overlapping covering design (NOCD) method, and then devise a dynamic programming method for determining the optimal parameters used in NOCD. Through privacy analysis, we show that DP-SUBN satisfies $\varepsilon$ ɛ -differential privacy. Extensive experiments on real datasets demonstrate that DP-SUBN offers desirable data utility with low communication cost. [ABSTRACT FROM AUTHOR]

The large-scale collection of individuals’ mobility data poses serious privacy concerns. Instead of perturbing data by adding noise to the raw location data to preserve privacy of individuals, we propose an approach that achieves privacy-preservation at the statistics level of aggregating mobility datasets with the probabilistic data structure Count-Min Sketch (CMS) , which has been widely used to provide efficient statistic functions with a tunable error bound. We use CMS to estimate the population density distributions in the mobility datasets, where the error bound determines utility guarantees. We develop P4Mobi, a novel Probabilistic Privacy-Preserving Publishing framework for Mobility datasets that protects individuals’ privacy while complying to a specific utility requirement. We empirically validate the performance of P4Mobi in terms of utility and privacy-preservation by demonstrating its resilience against a recently proposed reconstruction attack model using two real-world datasets. We compare P4Mobi to two state-of-the-art methods and show that with the same level of privacy achieved against our attack model, P4Mobi significantly improves the utility of the published mobility datasets by up to $20\%$. We also provide a theoretical estimate of the utility achieved by P4Mobi. We found a very consistent match between the estimated and empirical utility of P4Mobi as evaluated on two datasets. [ABSTRACT FROM AUTHOR]

We consider group-based anonymization schemes, a popular approach to data publishing. This approach aims at protecting privacy of the individuals involved in a dataset, by releasing an obfuscated version of the original data, where the exact correspondence between individuals and attribute values is hidden. When publishing data about individuals, one must typically balance the learner’s utility against the risk posed by an attacker, potentially targeting individuals in the dataset. Accordingly, we propose a unified Bayesian model of group-based schemes and a related MCMC methodology to learn the population parameters from an anonymized table. This allows one to analyze the risk for any individual in the dataset to be linked to a specific sensitive value, when the attacker knows the individual’s nonsensitive attributes, beyond what is implied for the general population. We call this relative threat analysis. Finally, we illustrate the results obtained with the proposed methodology on a real-world dataset. [ABSTRACT FROM AUTHOR]

This special issue is intended to inform the scientific computing community about recent advances and the current state of the art in software and data citation. Initial work has been done elsewhere to define standards and principles for software and data citation, and the basic required infrastructure is now in place. The challenge now is to adopt these practices. As their uptake increases, we believe that our professional culture will increasingly encourage production and sharing of software and data, leading to better and more reproducible and reusable results. The articles in this special issue discuss how software, data, and related digital objects are cited and otherwise mentioned in publications today, how to best cite them to make them accessible in the future, and what we can learn from the citations. [ABSTRACT FROM AUTHOR]

Following the widespread digitalization of scholarship, software has become essential for research, but the current sociotechnical system of citation does not reflect this sufficiently. Citation provides context for research, but the current model for the respective research citation graphs does not integrate software. In this article, I develop a directed graph model to alleviate this, describe challenges for its instantiation, and give an outlook of useful applications of research citation graphs, including transitive credit. [ABSTRACT FROM AUTHOR]

The universal popularity of GPS-enabled mobile devices and traffic navigation services has fueled the growth of trajectory data, as evidenced by Uber Movement and NYC taxi data release. Although trajectory data can generate valuable insights and value-added services for many, publishing this data while respecting mobile users' privacy has been a long-standing challenge. In this paper, we present DP-Star, a methodical framework for publishing trajectory data with differential privacy guarantee as well as high utility preservation. DP-Star relies on a novel combination of several components. First, DP-Star's normalization algorithm uses the Minimum Description Length metric to summarize raw trajectories using their representative points, thereby achieving a desirable trade-off between the preciseness and conciseness of their information content. Second, DP-Star constructs a density-aware grid which ensures spatial densities can be preserved despite the noise added to satisfy differential privacy. Third, DP-Star preserves the correlations between trajectories' end points through a private trip distribution, and intermediate points through a private Markov mobility model. Finally, DP-Star estimates users' trip lengths using a median length estimation method, and generates synthetic trajectories that preserve both differential privacy and high utility. Our experimental comparison shows that DP-Star significantly outperforms existing approaches in terms of trajectory utility and accuracy. [ABSTRACT FROM AUTHOR]

Presents a summary of articles published in Computer 50 and 25 years ago. [ABSTRACT FROM AUTHOR]

Trajectory data has been widely collected via mobile devices and publicly released for academic research and commercial purposes. One primary concern of publishing such a dataset is the privacy issue. Previous protection schemes mainly focus on preventing re-identification attack, which utilizes the uniqueness of trajectories. However, the correlation between trajectories, which has not been given much attention to before, could also give rise to serious privacy leakage. Recent studies have proved that it is possible to identify social relationship, de-anonymize trajectories or even infer user’s locations by analyzing the correlation between users’ trajectories. We identify the serious privacy problem of social relationship leakage caused by what we call social relationship attack and aim to protect social relationship information, which cannot be protected by existing algorithms. We contribute to the design of a new privacy model and an effective system to deal with social relationship attack and re-identification attack simultaneously while maintaining high data utility. We propose a SlidingWindow algorithm to merge trajectories according to their social-aware distance, which concerns both the spatiotemporal distance and social proximity. Evaluations of two trajectory datasets under different scenarios demonstrate that our system provides more than 1.84 times privacy protection at the cost of only 2.5% data utility loss. [ABSTRACT FROM AUTHOR]

In the 1960s, John and Jonathan Seybold founded and ran the pioneering computer typesetting company Rocappi. In 1971, they started the Seybold Report, which had a major role in guiding the computer transformation of publishing that took place in that decade. In 1981, Jonathan started the companion company, Seybold Seminars, to conduct conferences and (later) exhibitions, which facilitated the even broader revolutions in computing and publishing in the 1980s and 1990s. [ABSTRACT FROM AUTHOR]

Frame technology became a leading provider of technical publishing software within two years of its founding in 1986. Frame went public in February 1992, and by the end of that year, its flagship software, FrameMaker, ran on Apple Macintosh, Microsoft Windows, and all leading Unix workstations of that era. It was used by thousands of high-tech companies around the globe. The company was acquired by Adobe Systems in 1995 at a valuation of approximately $500 million. This paper is a personal remembrance by the founder who co-wrote FrameMaker 1.0, and lead Frame's product design and product strategy through its IPO. It tells Frame's founding story, and describes the key product and architectural features that contributed to its success. This paper also briefly describes Frame's early funding model, business and sales strategies, and culture. [ABSTRACT FROM AUTHOR]

Personalized recommendation is crucial to help users find pertinent information. It often relies on a large collection of user data, in particular users’ online activity (e.g., tagging/rating/checking-in) on social media, to mine user preference. However, releasing such user activity data makes users vulnerable to inference attacks, as private data (e.g., gender) can often be inferred from the users’ activity data. In this paper, we proposed PrivRank, a customizable and continuous privacy-preserving social media data publishing framework protecting users against inference attacks while enabling personalized ranking-based recommendations. Its key idea is to continuously obfuscate user activity data such that the privacy leakage of user-specified private data is minimized under a given data distortion budget, which bounds the ranking loss incurred from the data obfuscation process in order to preserve the utility of the data for enabling recommendations. An empirical evaluation on both synthetic and real-world datasets shows that our framework can efficiently provide effective and continuous protection of user-specified private data, while still preserving the utility of the obfuscated data for personalized ranking-based recommendation. Compared to state-of-the-art approaches, PrivRank achieves both a better privacy protection and a higher utility in all the ranking-based recommendation use cases we tested. [ABSTRACT FROM AUTHOR]

Nowadays, human trajectories are widely collected and utilized for scientific research and business purpose. However, publishing trajectory data without proper handling might cause severe privacy leakage. A large body of works is dedicated to merging one’s trajectory with others’, so as to avoid any individual trajectory being re-identified. Yet their solutions do not provide enough protection since they cannot prevent semantic attack, which means the attackers are able to acquire individual’s private information by using the semantics features of frequently visited locations in the trajectory even without re-identification. In this paper, we are the first to recognize the semantic attack, which is another severe privacy problem in publishing trajectory datasets. We propose an algorithm providing strong privacy protection against both the semantic and re-identification attack while reserving high data utility. Extensive evaluations based on two real-world datasets demonstrate that our solution improves the quality of privacy protection by three times, sacrificing only 36% and 10% of spatial and temporal resolution, respectively. [ABSTRACT FROM AUTHOR]

Scientists striving for impact in their fields and to develop their own careers must publish papers that represent new and important science, typically in a peer-reviewed journal. The number of scientific articles published has doubled every nine years since WWII, and now stands at more than 3 million peer-reviewed articles annually from more than 34,000 scholarly journals. [ABSTRACT FROM AUTHOR]

As it continues to proliferate, malware has shown increasing sophistication, and PDF malware is a major threat on the cybersecurity landscape. We provide an overview of current attack techniques used to convey PDF malware and the analysis tools that support digital forensic investigations. [ABSTRACT FROM AUTHOR]

The IEEE Transactions on Automation Science and Engineering (T-ASE) wishes to thank the 959 reviewers over the past year who have performed an essential role in maintaining the quality of this publication. T-ASE strives for the 90–90 standard, i.e., 90% of papers should be reviewed within 90 days of submission. The review process starts when the Editor-in-Chief assigns the paper to an Editor, who assigns it to an Associate Editor (AE). The AE then obtains confirmation from 3–5 reviewers, carefully studies the results, and makes a recommendation to the Editor, who then makes a decision that is reported back to the lead author. This review process is very demanding, especially under the time constraints. We have summarized the process under “Guidelines for T-ASE Editors and AEs for Handling Papers” on our website. I am pleased to report that submissions have steadily increased (over 1000 in 2017) and our Impact Factor is now 3.667, a 12.2% increase over last year. As we strive to increase the quality and impact of the journal, we rely on the hard work of our reviewers and members of the Editorial Board. To those dedicated volunteers, and to anyone who may have been inadvertently omitted from the list below, we extend our sincere appreciation. [ABSTRACT FROM AUTHOR]

Mobile and wireless communications are playing an irreplaceable role in our daily life. 5G is highly expected and can be considered as the convergence of mobile and wireless communications. This topic becomes one of the hottest research topics recently, and papers on this topic have been paid much attention. lists the top ten papers published in the IEEE Transactions on Microwave Theory and Techniques (TMTT) in 2019 with the highest numbers of Full Text View since published. Seven papers are about millimeter-waves (mm-waves), and because mm-waves multiple-input–multiple-output (MIMO) will be adopted globally, the 24.25–27.5-GHz band and 27–29.5-GHz bands are now approved worldwide as the standard 5G communication bands. The most popular paper is published by Asbeck et al. [item 1) in the Appendix] for mm-wave power amplifiers. [ABSTRACT FROM AUTHOR]

The increasing interest in collecting and publishing large amounts of individuals’ data as public for purposes such as medical research, market analysis, and economical measures has created major privacy concerns about individual's sensitive information. To deal with these concerns, many Privacy-Preserving Data Publishing (PPDP) techniques have been proposed in literature. However, they lack a proper privacy characterization and measurement. In this paper, we first present a novel multi-variable privacy characterization and quantification model. Based on this model, we are able to analyze the prior and posterior adversarial belief about attribute values of individuals. We can also analyze the sensitivity of any identifier in privacy characterization. Then, we show that privacy should not be measured based on one metric. We demonstrate how this could result in privacy misjudgment. We propose two different metrics for quantification of privacy leakage, distribution leakage, and entropy leakage. Using these metrics, we analyzed some of the most well-known PPDP techniques such as $k$ -anonymity, $l$ -diversity, and $t$ -closeness. Based on our framework and the proposed metrics, we can determine that all the existing PPDP schemes have limitations in privacy characterization. Our proposed privacy characterization and measurement framework contributes to better understanding and evaluation of these techniques. Thus, this paper provides a foundation for design and analysis of PPDP schemes. [ABSTRACT FROM AUTHOR]

In this issue of the Annals, we have two Anecdotes on the history of Atex, an early innovator in newspapers and magazines converting to digital operation. The first anecdote is by co-founder Douglas Drane. This anecdote provides more information about Atexs history. [ABSTRACT FROM AUTHOR]

Donald Knuth began the development of TeX in 1977 and had an initial version running in 1978, with the aim of typesetting mathematical documents with the highest quality, and with archival reproducibility far into the future. Its usage spread, and today the TeX system remains in use by a vibrant user community. However, the world of TeX has always been somewhat out of sync with the commercial desktop publishing systems that began to come out in the mid-1980s and are now ubiquitous in the worlds of publishing and printing. [ABSTRACT FROM AUTHOR]

Guest editors Burton Grad and David Hemmendinger discuss the History of Desktop Publishing, Part 1 issue. [ABSTRACT FROM AUTHOR]

Introduced by Adobe Systems in 1984, PostScript was a unique device-independent approach to describing the appearance of a printed page, for the first time allowing pages to be printed on a range of devices of different resolutions. From 1984 to 1987, the use of PostScript by printer manufacturers grew to the point that it became the de facto standard. [ABSTRACT FROM AUTHOR]

Several word processing and desktop publishing innovations grew out of 1970s research by Larry Tesler and his collaborators at Stanford and Xerox PARC. One prototype was PUB, a markup language for print publishing. Another was Gypsy, a text editor with a cut/copy/paste command suite that became the de facto standard in desktop publishing and most other applications. [ABSTRACT FROM AUTHOR]

In 1963, John Seybold started the company Rocappi to use computer technology to produce and typeset complex, high-quality books, catalogs, directories, manuals, magazines, and other typeset documents. Over the next seven years, with the participation of his son, Jonathan W. Seybold, Rocappi pioneered the use of computers for publishing. [ABSTRACT FROM AUTHOR]

Starting in 1973, researchers at the Xerox Palo Alto Research Center (PARC) built the first networked workstation platform (Alto and Ethernet) and a suite of applications and services to explore office automation. Separate interactive tools allowed people to create and edit different sorts of document content, which were then merged onto pages using Press files, a page description file format. [ABSTRACT FROM AUTHOR]

Many realistic wireless social networks are evolving over time. While network evolution has its important influence on network performances, it is nevertheless overlooked in most existing studies on information diffusion. Motivated by this, in this paper, we investigate the delivery accuracy of interest-aware information diffusion in evolving social networks. In doing so, we adopt a model, named affiliation networks, to characterize network evolution from three aspects, i.e., the arrival of new users, the generation of new interests, and the creation of new links between them. Based on that, we consider a publishing based information diffusion mechanism that widely exists in wireless networking services such as Facebook, Twitter, and Sina Weibo, where a user receives data items from his friends and then republishes the ones he is interested in to all his friends. Under the above network model, we study how the performance metric such as delivery accuracy is affected by the network evolution. The publishing based information diffusion mechanism is a blind targeting one that may suffer a low delivery accuracy. However, our analytical results demonstrate a contrary finding that the delivery accuracy is improved over time, and even more surprisingly, we disclose that with a sufficiently long evolving time, the delivery accuracy can achieve a perfect state where those who receive the data are exactly the ones that are interested in it. In addition, our theoretical findings are verified by experimental measurements through a social network dataset from Facebook. [ABSTRACT FROM AUTHOR]

Human mobility data have been ubiquitously collected through cellular networks and mobile applications, and publicly released for academic research and commercial purposes for the last decade. Since releasing individual’s mobility records usually gives rise to privacy issues, data sets owners tend to only publish aggregated mobility data, such as the number of users covered by a cellular tower at a specific timestamp, which is believed to be sufficient for preserving users’ privacy. However, in this paper, we argue and prove that even publishing aggregated mobility data could lead to privacy breach in individuals’ trajectories. We develop an attack system that is able to exploit the uniqueness and regularity of human mobility to recover individual’s trajectories from the aggregated mobility data without any prior knowledge. By conducting experiments on two real-world data sets collected from both the mobile application and cellular network, we reveal that the attack system is able to recover users’ trajectories with an accuracy of about 73%~91% at the scale of thousands to ten thousands of mobile users, which indicates severe privacy leakage in such data sets. Our extensive analysis also reveals that by generalization and perturbation, this kind of privacy leakage can only be mitigated. Through the investigation on aggregated mobility data, this paper recognizes a novel privacy problem in publishing statistic data, which appeals for immediate attentions from both the academy and industry. [ABSTRACT FROM AUTHOR]

Graphs are used for representing and understanding objects and their relationships for numerous applications such as social networks, Semantic Webs, and biological networks. Integrity assurance of data and query results for graph databases is an essential security requirement. In this paper, we propose two efficient integrity verification schemes—HMACs for graphs (gHMAC) for two-party data sharing, and redactable HMACs for graphs (rgHMAC) for third-party data sharing, such as a cloud-based graph database service. We compute one HMAC value for both the schemes and two other verification objects for rgHMAC scheme that are shared with the verifier. We show that the proposed schemes are provably secure with respect to integrity attacks on the structure and/or content of graphs and query results. The proposed schemes have linear complexity in terms of the number of vertices and edges in the graphs, which is shown to be optimal. Our experimental results corroborate that the proposed HMAC-based schemes for graphs are highly efficient as compared to the digital signature-based schemes—computation of HMAC tags is about 10 times faster than the computation of digital signatures. [ABSTRACT FROM PUBLISHER]

The authors introduce The Journal of Open Source Software (JOSS), a publication that focuses on research software and its place in the scholarly publishing ecosystem. JOSSs goal is to make it easy for authors to publish a paper about their software, mostly focused on the software itself, and then be credited when this software is used based on users citing the JOSS paper. [ABSTRACT FROM AUTHOR]

Hypothesis testing is a statistical inference framework for determining the true distribution among a set of possible distributions for a given data set. Privacy restrictions may require the curator of the data or the respondents themselves to share data with the test only after applying a randomizing privacy mechanism. This work considers mutual information (MI) as the privacy metric for measuring leakage. In addition, motivated by the Chernoff-Stein lemma, the relative entropy between pairs of distributions of the output (generated by the privacy mechanism) is chosen as the utility metric. For these metrics, the goal is to find the optimal privacy-utility tradeoff (PUT) and the corresponding optimal privacy mechanism for both binary and $m$ -ary hypothesis testing. Focusing on the high privacy regime, Euclidean information-theoretic approximations of the binary and $m$ -ary PUT problems are developed. The solutions for the approximation problems clarify that an MI-based privacy metric preserves the privacy of the source symbols in inverse proportion to their likelihoods. [ABSTRACT FROM PUBLISHER]

A social approach can be exploited for the Internet of Things (IoT) to manage a large number of connected objects. These objects operate as autonomous agents to request and provide information and services to users. Establishing trustworthy relationships among the objects greatly improves the effectiveness of node interaction in the social IoT and helps nodes overcome perceptions of uncertainty and risk. However, there are limitations in the existing trust models. In this paper, a comprehensive model of trust is proposed that is tailored to the social IoT. The model includes ingredients such as trustor, trustee, goal, trustworthiness evaluation, decision, action, result, and context. Building on this trust model, we clarify the concepts of trust in the social IoT in five aspects such as: 1) mutuality of trustor and trustee; 2) inferential transfer of trust; 3) transitivity of trust; 4) trustworthiness update; and 5) trustworthiness affected by dynamic environment. With network connectivities that are from real-world social networks, a series of simulations are conducted to evaluate the performance of the social IoT operated with the proposed trust model. An experimental IoT network is used to further validate the proposed trust model. [ABSTRACT FROM PUBLISHER]