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The rapid growth of social media in the era of big data and artificial intelligence has raised significant safety concerns related to the communication of sensitive personal information. In modern society, awareness of the importance of preserving privacy is growing, so there is a rising advocacy for adopting language modeling technology to mitigate the risk of personal information leakage and to deidentify sensitive information depending on the situation. Thus far, several theoretical analyses of privacy protection in Korea have been conducted. However, the technical development of language model training resources for Korean has been slower than those of widely spoken languages such as English and Chinese. To address this problem, we developed a comprehensive and organized framework for classifying Korean personally identifiable information (PII) by investigating pertinent examples, such as “Text Anonymization Benchmark” and “Network Intrusion Detection Dataset,” from within and outside Korea. Subsequently, we created a new Korean dataset for PII deidentification, KDPII, which consists of many conversational texts incorporating plentiful Korean PII. Based on this, we examined the Korean PII processing performances of many representative language models that are available on the market. Finally, we found that although the performance of language models in identifying PII varied by model size, model architecture, and training source, most of them were significantly better at recognizing universal PII than language-specific PII, which indicates a prospective direction of expanding training data for implementing Korean-specific PII deidentification in the future.

The current age is witnessing an unprecedented dependence on data originating from humans through the devices that comprise the Internet of Things. The data collected by these devices are used for many purposes, including predictive maintenance, smart analytics, preventive healthcare, disaster protection, and increased operational efficiency and performance. However, most applications and systems that rely on user data to achieve their business objectives fail to comply with privacy regulations and expose users to numerous privacy threats. Such privacy breaches raise concerns about the legitimacy of the data being processed. Hence, this paper reviews some notable techniques for transparently, securely, and privately separating and sharing personally identifiable and non-personally identifiable information in various domains. One of the key findings of this study is that, despite various advantages, none of the existing techniques or data sharing applications preserve data/user privacy throughout the data life cycle. Another significant issue is the lack of transparency for data subjects during the collection, storage, and processing of private data. In addition, as privacy is unique to every user, there cannot be a single autonomous solution to identify and secure personally identifiable information for users of a particular application, system, or people living in different states/countries. Therefore, this research suggests a way forward to prevent the leakage of personally identifiable information at various stages of the data life cycle in compliance with some of the common privacy regulations around the world. The proposed approach aims to empower data owners to select, share, monitor, and control access to their data. In addition, the data owner is a stakeholder and a party to all data sharing contracts related to his personal data. The proposed solution has broad security and privacy controls that can be tailored to the privacy needs of specific applications.

Fake check scam is one of the most common attacks used to commit fraud against consumers. This fraud is particularly costly for victims because they generally lose thousands of dollars as well as being exposed to judicial proceedings. Currently, there is no existing solution to authenticate checks and detect fake ones instantly. Instead, banks must wait for a period of more than 48 h to detect the scam. In this context, we propose a blockchain-based scheme to authenticate checks and detect fake check scams. Moreover, our approach allows the revocation of used checks. More precisely, our approach helps the banks to share information about provided checks and used ones, without exposing the banks’ customers’ personal data. We demonstrate a proof of concept of our proposed approach using Namecoin and Hyperledger blockchain technologies. [ABSTRACT FROM AUTHOR]

In order to improve mobile data transparency, various approaches have been proposed to inspect network traffic generated by mobile devices and detect exposure of personally identifiable information (PII), ad requests, etc. State-of-the-art approaches use features extracted from HTTP packets and train classifiers in a centralized way: users collect and label network packets on their mobile devices, then upload data to a central server; the server uses the data contributed by all users to train a packet classifier. However, training datasets from network traffic collected on user devices may contain sensitive information that users may not want to upload. In this article, we propose a federated learning approach to mobile packet classification, which enables devices to collaboratively train a global model, without uploading the training data collected on devices. We apply our framework to two packet classification tasks (i.e., to predict PII exposure or ad requests in individual packets) and we demonstrate its effectiveness in terms of classification performance, communication and computation cost, using three real-world datasets. Methodological challenges we address in the process include model and feature selection, as well as tuning the federated learning parameters specifically for our packet classification tasks. We also discuss privacy limitations and mitigation approaches. [ABSTRACT FROM AUTHOR]

Although the use of personal data from different contexts is essential to curbing the spread of COVID-19 in epidemic-handling systems (EHSs), it increases the chances of privacy breaches and personal data misuse. This article analyses the data lifecycle and proposes various technical requirements for privacy preservation in EHSs. [ABSTRACT FROM AUTHOR]

As deep learning applications often encounter accuracy degradation due to the distorted inputs from a variety of environmental conditions, training with personal data has become essential for the edge devices. Hence, ‘training on edge’ by supporting a trainable deep learning accelerator has been actively studied. Nevertheless, previous research does not consider the fundamental datapath for training and the importance of retaining the high performance for inference tasks. In this work, we propose NeuroFlix, a deep neural network training accelerator supporting heterogeneous data-type of floating- and fixed-point for input operands. From two perspectives: 1) separate precision decision for each input data, 2) maintenance of high performance on inference, we configure the data with low-bit fixed-point of activation/weight and floating-point based error gradient securing up to half-precision. A novel MAC architecture is designed to compute low- and high-precision modes for the different input combinations. By substituting a high-cost floating-point based addition to brick-level separate accumulations, we realize both area-efficient architecture and high throughput for low-precision computation. Consequently, NeuroFlix outperforms the previous architectures of state-of-the-art configurations proving its high efficiency in both training and inference. By also comparing with the off-the-shelf bfloat16-based accelerator, it achieves 1.2 ×/2.0 × of speedup/energy-efficiency at training and further enhancement of 3.6 ×/4.5 × at inference. [ABSTRACT FROM AUTHOR]

The society’s insatiable appetites for personal data are driving the emergence of data markets, allowing data consumers to launch customized queries over the datasets collected by a data broker from data owners. In this paper, we study how the data broker can maximize its cumulative revenue by posting reasonable prices for sequential queries. We thus propose a contextual dynamic pricing mechanism with the reserve price constraint, which features the properties of ellipsoid for efficient online optimization and can support linear and non-linear market value models with uncertainty. In particular, under low uncertainty, the proposed pricing mechanism attains a worst-case cumulative regret logarithmic in the number of queries. We further extend our approach to support other similar application scenarios, including hospitality service and online advertising, and extensively evaluate all three use cases over MovieLens 20M dataset, Airbnb listings in U.S. major cities, and Avazu mobile ad click dataset, respectively. The analysis and evaluation results reveal that: (1) our pricing mechanism incurs low practical regret, while the latency and memory overhead incurred is low enough for online applications; and (2) the existence of reserve price can mitigate the cold-start problem in a posted price mechanism, thereby reducing the cumulative regret. [ABSTRACT FROM AUTHOR]

In the digital forensics discipline, the lack of comprehensive research that addresses investigative challenges and opportunities for newer mobile Operating Systems (OSs) such as Android and iOS keeps continuing. These two OSs are currently widely operated by millions of smartphones and used by millions of users; therefore, forensic investigators need to be prepared to analyze these OSs during an investigation giving consideration to mobile app updates. The current research efforts focus on the forensic analysis of individual applications of certain OSs. In this study, we conducted a detailed forensic investigation of both Android and iOS OSs to (1) elucidate their structures for investigators, (2) identify pertinent forensic artifacts, (3) highlight any privacy and security concerns in popular applications present on both OSs, and (4) validate the forensic investigation on the selected tools for reproducibility and verification purposes. This work aims to analyze 27 Android and 33 iOS mobile applications comprehensively.

The concept of identity has become one common research topic in security and privacy where the real identity of users must be preserved, usually covered by pseudonym identifiers. With the rise of Blockchain-based systems, identities are becoming even more critical than before, mainly due to the immutability property. In fact, many publicly accessible Blockchain networks like Ethereum rely on pseudonymization as a method for identifying subject actions. Pseudonyms are often employed to maintain anonymity, but true anonymity requires unlinkability. Without this property, any attacker can examine the messages sent by a specific pseudonym and learn new information about the holder of this pseudonym. This use of Blockchain collides with regulations because of the right to be forgotten, and Blockchain-based solutions are ensuring that every data stored within the chain will not be modified. In this paper we define a method and a tool for dealing with digital identities within Blockchain environments that are compliant with regulations. The proposed method provides a way to grant digital pseudo identities unlinked to the real identity. This new method uses the benefits of key derivation systems to ensure a non-binding interaction between users and the information model associated with their identity. The proposed method is demonstated in the Ethereum context and illustrated with a case study.

Personal informatics research helps people track personal data for the purposes of self-reflection and gaining self-knowledge. This field, however, has predominantly focused on the data collection and insight-generation elements of self-tracking, with less attention paid to flexible data analysis. As a result, this inattention has led to inflexible analytic pipelines that do not reflect or support the diverse ways people want to engage with their data. This paper contributes a review of personal informatics and visualization research literature to expose a gap in our knowledge for designing flexible tools that assist people engaging with and analyzing personal data in personal contexts, what we call the personal informatics analysis gap. We explore this gap through a multistage longitudinal study on how asthmatics engage with personal air quality data, and we report how participants: were motivated by broad and diverse goals; exhibited patterns in the way they explored their data; engaged with their data in playful ways; discovered new insights through serendipitous exploration; and were reluctant to use analysis tools on their own. These results present new opportunities for visual analysis research and suggest the need for fundamental shifts in how and what we design when supporting personal data analysis. [ABSTRACT FROM AUTHOR]

Health is the most important aspect of life. However, health is the least developed aspect of modern society that increasingly uses data as a major resource for understanding complexities in a system and relies on predictive control for the overall improvement of systems. Progress in biology, multimodal sensors, mobile devices, computing, and related areas is rapidly establishing that health is a perpetual personal data system rather than current population-based episodic disease-oriented healthcare approach. Lifestyle is more important for controlling health than medicine. Technology now allows us to perpetually measure lifestyle and analyze it to build personal models and estimate health state for developing predictive, preventive, personalized approach for maximizing healthiness for most people in the world. These emerging approaches have demonstrated their early success and are now ready for helping people achieve their health goals. Data is the lifeblood of health; technology has a great opportunity to bring this imminent transformation now. [ABSTRACT FROM AUTHOR]

Trust in the governance of technologies—distinct from trust in the technologies themselves—is a critical factor in the protection of society and the flourishing of innovation. Governance failures—the risk management of asbestos or mismanagement of personal data by Facebook and Cambridge Analytica among others—show how inadequacies of governance can erode trust. This erosion can harm not only the development of useful technologies, but also potentially the entire system of governance that protects the rule of law and forms the basis of modern societies. [ABSTRACT FROM AUTHOR]

Background: Many countries mandate transparency and consent when personal data are handled by online services. However, most users do not read privacy policies or cannot understand them. An important challenge for technical communicators is empowering users to manage their online privacy responsibly. Literature review: Research suggests that privacy visualizations may alleviate this problem, but existing approaches are incomplete and under-researched. Research questions: 1. How can we design a privacy rating that optimally empowers users with different levels of knowledge about and awareness of online privacy? 2. How do users react to such a privacy rating, in terms of usability, perceived usefulness, and trust in online services? Methodology: We developed Privacy Rating, a tool for mapping and visualizing the privacy of online services. The tool was subjected to user research (N = 30) focusing on usability, perceived usefulness, and effects on trust. To establish the effects on trust, participants were exposed to a website with either a positive or a negative privacy rating. Results: The Privacy Rating appeared to be usable and useful for lay users, and it had a significant effect on users’ trust in the online service. Users indicated that they would like the visualization to become an established standard, preferably approved by an independent organization. Conclusions: The Privacy Rating is a user-friendly privacy visualization covering all relevant aspects of privacy. We aim to bring the tool to the market and make it a standard, ideally supported by an independent trustworthy organization. [ABSTRACT FROM AUTHOR]

Background: Data breaches happen when an unauthorized party gains access to personally identifiable information. They are becoming more common and impactful, raising serious concerns for individuals as well as companies. Literature review: Although there is considerable literature on users’ mental models in security and privacy, there has been limited study of mental models related to data breaches. Research questions: 1. How do users understand data breaches? 2. What are their perceptions of the causes, responsibilities, and consequences, as well as possible prevention and appropriate follow up? Methodology: We explored end-user understanding of internet data breaches by conducting a study with 35 participants. They were asked to draw their understanding of data breaches and answer some open-ended and closed-ended questions afterwards. Results/discussion: Although their drawings varied in detail and complexity, we identified four patterns in the participants’ drawings: they illustrated abstractions of attacks to gain administrator access, end-user access, backdoor access, or access using database server vulnerabilities. We found that participants had a basic model of how an internet data breach happens, but with significant uncertainties regarding system vulnerabilities, causes, consequences, prevention methods, and follow-up steps after a breach. Conclusions: In all, end-user mental models of internet data breaches are basic and show gaps that emphasize the need for improved communication to increase users’ awareness and help them hold companies accountable. [ABSTRACT FROM AUTHOR]

I describe the results of implementing a personal data physicalization assignment in an information visualization course for senior undergraduate and graduate students in computer science and software engineering. By collecting data about themselves and representing this data in physical forms, students were able to 1) learn about data visualization, 2) design creatively, and 3) learn about themselves. While data physicalization in education has been explored with nontechnical and novice audiences, the experience I report in this article provides evidence that data physicalization also has benefits for technical, visualization-savvy students. [ABSTRACT FROM AUTHOR]

The issue of potential privacy leakage during centralized AI’s model training has drawn intensive concern from the public. A Parallel and Distributed Computing (or PDC) scheme, termed Federated Learning (FL), has emerged as a new paradigm to cope with the privacy issue by allowing clients to perform model training locally, without the necessity to upload their personal sensitive data. In FL, the number of clients could be sufficiently large, but the bandwidth available for model distribution and re-upload is quite limited, making it sensible to only involve part of the volunteers to participate in the training process. The client selection policy is critical to an FL process in terms of training efficiency, the final model’s quality as well as fairness. In this article, we will model the fairness guaranteed client selection as a Lyapunov optimization problem and then a C2MAB-based method is proposed for estimation of the model exchange time between each client and the server, based on which we design a fairness guaranteed algorithm termed RBCS-F for problem-solving. The regret of RBCS-F is strictly bounded by a finite constant, justifying its theoretical feasibility. Barring the theoretical results, more empirical data can be derived from our real training experiments on public datasets. [ABSTRACT FROM AUTHOR]

Smart wristband has become a dominant device in the wearable ecosystem, providing versatile functions such as fitness tracking, mobile payment, and transport ticketing. However, the small form-factor, low-profile hardware interfaces and computational resources limit their capabilities in security checking. Many wristband devices have recently witnessed alarming vulnerabilities, e.g., personal data leakage and payment fraud, due to the lack of authentication and access control. To fill this gap, we propose a secure text pin input system, namely Taprint, which extends a virtual number pad on the back of a user's hand. Taprint builds on the key observation that the hand “landmarks”, especially finger knuckles, bear unique vibration characteristics when being tapped by the user herself. It thus uses the tapping vibrometry as biometrics to authenticate the user, while distinguishing the tapping locations. Taprint reuses the inertial measurement unit in the wristband, “overclocks” its sampling rate with the cubic spline interpolation to extrapolate fine-grained features, and further refines the features to enhance the uniqueness and reliability. Extensive experiments on 128 users demonstrate that Taprint achieves a high accuracy (96 percent) of keystrokes recognition. It can authenticate users, even through a single-tap, at extremely low error rate (2.2 percent), and under various practical usage disturbances. [ABSTRACT FROM AUTHOR]

Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents TransparencyVis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources. [ABSTRACT FROM AUTHOR]

Physical engagement with data necessarily influences the reflective process. However, the role of interactivity and narration are often overlooked when designing and analyzing personal data physicalizations. We introduce Narrative Physicalizations, everyday objects modified to support nuanced self-reflection through embodied engagement with personal data. Narrative physicalizations borrow from narrative visualizations, storytelling with graphs, and engagement with mundane artifacts from data-objects. Our research uses a participatory approach to research-through-design and includes two interdependent studies. In the first, personalized data physicalizations are developed for three individuals. In the second, we conduct a parallel autobiographical exploration of what constitutes personal data when using a Fitbit. Our work expands the landscape of data physicalization by introducing narrative physicalizations. It suggests an experience-centric view on data physicalization where people engage physically with their data in playful ways, making their body an active agent during the reflective process. [ABSTRACT FROM AUTHOR]

Never before has cybersecurity been as significant for societies as today; its importance is still on the rise as people rely on computer systems every day. Recently, the ever-growing issues of personal data being stolen or misused have been addressed by several legal regulations, the most significant of them being the General Data Protection Regulation (GDPR). The paper deals with a specific cybersecurity measure, malware/stegomalware detection. This solution, which is meant to protect personal data from being used in a way that breaks the GDPR, has to be GDPR-compliant itself. This work describes the GDPR regulation in detail, indicates the specific challenges that may arise when applying malware/stegomalware solutions, and provides several recommendations for ensuring compliance with the regulation. [ABSTRACT FROM AUTHOR]

Universal Serial Bus (USB) is widely used, for example to facilitate hot-swapping and plug-and-play. However, USB ports can be exploited by an adversary to extract private or personal data from the connected devices. Hence, a number of organizations and workplaces have prohibited their employees from using USB devices, and there have been efforts to design secure USB storage device schemes to more effectively resist different known security attacks. However, designing such schemes is challenging. For example, in this article we revisit the Wei et al.’s scheme, and demonstrate that it is vulnerable to attacks such as password guessing and user impersonation. We also explain that the scheme does not verify the correctness of user’s input in the login phase, which is another design flaw. Then, we present an improved scheme and prove it secure in the random oracle model. [ABSTRACT FROM AUTHOR]

The growing number of mobile and IoT devices has nourished many intelligent applications. In order to produce high-quality machine learning models, they constantly access and collect rich personal data such as photos, browsing history, and text messages. However, direct access to personal data has raised increasing public concerns about privacy risks and security breaches. To address these concerns, there are two emerging solutions to privacy-preserving machine learning, namely local differential privacy and federated machine learning. The former is a distributed data collection strategy where each client perturbs data locally before submitting to the server, whereas the latter is a distributed machine learning strategy to train models on mobile devices locally and merge their output (e.g., parameter updates of a model) through a control protocol. In this article, we conduct a comparative study on the efficiency and privacy of both solutions. Our results show that in a standard population and domain setting, both can achieve an optimal misclassification rate lower than 20% and federated machine learning generally performs better at the cost of higher client CPU usage. Nonetheless, local differential privacy can benefit more from a larger client population ($>$> 1k). As for privacy guarantee, local differential privacy also has flexible control over the data leakage. [ABSTRACT FROM AUTHOR]

With the rapid development of computing technology, wearable devices make it easy to get access to people's health information. Smart healthcare achieves great success by training machine learning models on a large quantity of user personal data. However, there are two critical challenges. First, user data often exist in the form of isolated islands, making it difficult to perform aggregation without compromising privacy security. Second, the models trained on the cloud fail on personalization. In this article, we propose FedHealth, the first federated transfer learning framework for wearable healthcare to tackle these challenges. FedHealth performs data aggregation through federated learning, and then builds relatively personalized models by transfer learning. Wearable activity recognition experiments and real Parkinson's disease auxiliary diagnosis application have evaluated that FedHealth is able to achieve accurate and personalized healthcare without compromising privacy and security. FedHealth is general and extensible in many healthcare applications. [ABSTRACT FROM AUTHOR]

The visualization research community can and should reach broader audiences beyond data-savvy groups of people, because these audiences could also greatly benefit from visual access to data. In this article, we discuss four research topics—personal data visualization, data visualization on mobile devices, inclusive data visualization, and multimodal interaction for data visualization—that, individually and collaboratively, would help us reach broader audiences with data visualization, making data more accessible. [ABSTRACT FROM AUTHOR]

With the accumulation of data on personal behavior and the development of machine learning models and algorithms, it is becoming possible to use social media data for personal credit scoring. In this article, we use the systematic sampling method to obtain Douban's social media data. Because there are many abnormal users in these data, they are "real but false data" for personal credit evaluation. In order to better carry out personal credit scoring, we propose three criteria, power exponents of time interval distribution of individual user $\gamma _i$γi, user activity $A_i$Ai, and the ratio of out-degree and in-degree $R_i$Ri of user $i$i, which are used to systematically clean the data. And then, we used the logistic regression method to score the individual credits of users before and after data cleaning, and found that the rank order of personal credit scoring has changed significantly. This change is largely attributed to the changes of network structure after data cleaning. We believe that our work is very important to use the social media data to establish a credible personal credit evaluation system to reduce the credit risk of the current Internet financial industry. [ABSTRACT FROM AUTHOR]

Vehicular cloud computing (VCC) is composed of multiple distributed vehicular clouds (VCs), which are formed on-the-fly by dynamically integrating underutilized vehicular resources including computing power, storage, and so on. Existing proposals for identity-as-a-service (IDaaS) are not suitable for use in VCC due to limited computing resources and storage capacity of onboard vehicle devices. In this paper, we first propose an improved ciphertext-policy attribute-based encryption (CP-ABE) scheme. Utilizing the improved CP-ABE scheme and the permissioned blockchain technology, we propose a lightweight and privacy-preserving IDaaS architecture for VCC named IDaaSoVCC. It realizes lightweight and privacy-preserving access control of vehicles’ personally identifiable information (PII) in a large distributed vehicular cloud system. Security analysis demonstrates the security features of IDaaSoVCC, most notably forward secrecy, confidentiality and identity information privacy. Meanwhile, we verify that IDaaSoVCC is feasible and practical in a large distributed VC system through extensive simulations. [ABSTRACT FROM AUTHOR]

With the advent of Internet of Things (IoT) technologies and availability of a large amount of data, deep learning has been applied in a variety of artificial intelligence (AI) applications. However, sharing personal data using IoT edge devices carries inherent risks to individual privacy. Meanwhile, the energy and memory resources needed during the inference process become a constraint to the resource-limited IoT edge devices. This article brings memory hardware optimization to meet the tight power budget in IoT edge devices by considering the privacy, accuracy, and power efficiency tradeoff in differentially efficient deep learning systems. Based on a detailed analysis on these characteristics, an integer linear programs (ILP) model is developed to minimize mean square error (MSE), thereby enabling optimal input data memory design. Our simulation results in 45-nm CMOS technology show that the proposed technique can enable near-threshold energy-efficient memory operation for different privacy requirements, with less than 1% degradation in classification accuracy. [ABSTRACT FROM AUTHOR]

Because of collecting a large amount of personal data, when the artificial neural network (ANN) is used in human-related topics, it has raised great concerns on privacy preservation. A robust solution is to introduce a noise injection mechanism as differential privacy that promises strong theoretical privacy guarantees. However, privacy-preserving ANN with noisy input data has a substantial risk of reducing the recognition accuracy. Therefore, it is urgently needed to have technologies that can make users’ data applied to neural networks while strictly protecting sensitive information. In this paper, a linear optimization (LO) method is proposed to address this accuracy degradation by optimizing the performance of memristor in weight updating processes. Instead of complying with the traditional hardware and algorithm, the LO method calculates update parameters along a piecewise line by using different input pulses. The proposed method can mitigate the nonlinear problem of memristor without prereading the precise current conductance each time, thereby avoiding complex peripheral circuits. The effectiveness of the proposed LO method with two-segment, three-segment, and four-segment models is investigated, respectively. The results show that under different nonlinearity and different perturbation noise required by differential privacy theory, the LO method can increase the recognition accuracy of Modified National Institute of Standards and Technology (MNIST) handwriting digits by 39.67% on average, which provides more space and margin for privacy-preserving technology. [ABSTRACT FROM AUTHOR]

With the development of easily wearable devices for humans, monitoring, and collecting lifelog data related to personal health status is easier than ever before. The advances in such wearable devices allow healthcare service providers to easily collect a vast amount of health lifelogs from diverse users for the purpose of data analysis. However, collecting health information of individual users indiscriminately may lead to serious privacy issues, because health lifelog data usually contain sensitive information. Thus, in this paper, we develop methods capable of collecting sensitive health lifelogs from a smartwatch, which is the most popular wearable device, while protecting the data privacy of smartwatch users. Experimental results show that the proposed approach can achieve an effective tradeoff between the degree of privacy protection and the accuracy in aggregate statistics. A correlation coefficient with an absolute value ranging from 0.808 to 0.945 between the degree of privacy protection and the accuracy in aggregate statistics can be accomplished using the proposed methods. [ABSTRACT FROM AUTHOR]

We explore the use of longitudinal dialog data for two dialog prediction tasks: next message prediction and response time prediction. We show that a neural model using personal data that leverages a combination of message content, style matching, time features, and speaker attributes leads to the best results for both tasks, with error rate reductions of up to 15% compared to a classifier that relies exclusively on message content and to a classifier that does not use personal data. [ABSTRACT FROM AUTHOR]

Transparency is often seen as a means to provide accountability and show that something is done with due diligence. This approach to transparency regards it as a remedy to hidden (potentially malevolent) practices. We, therefore, require transparency from manufacturers of products and services. While the outcry for more transparency often occurs in response to a particular scandal (e.g., the various controversies surrounding Facebook in 2018), the European Union's General Data Protection Regulation (GDPR) includes transparency as a proactive requirement for information technologies that process personal data. [ABSTRACT FROM AUTHOR]

We examine the potential for immersive unit visualizations—interactive virtual environments populated with objects representing individual items in a dataset. Our virtual reality prototype highlights how immersive unit visualizations can allow viewers to examine data at multiple scales, support immersive exploration, and create affective personal experiences with data. [ABSTRACT FROM AUTHOR]

With the revelation that Facebook handed over personally identifiable information of more than 87 million users to Cambridge Analytica, it is now imperative that comprehensive privacy policy laws be developed. Technologists, researchers, and innovators should meaningfully contribute to the development of these policies. [ABSTRACT FROM AUTHOR]

Private information retrieval (PIR) is the problem of retrieving one message out of $K$ messages from $N$ non-communicating replicated databases, where each database stores all $K$ messages, in such a way that each database learns no information about which message is being retrieved. The capacity of PIR is the maximum number of bits of desired information per bit of downloaded information among all PIR schemes. The capacity has recently been characterized for PIR as well as several of its variants. In every case it is assumed that all the queries are generated by the user simultaneously. Here we consider multiround PIR, where the queries in each round are allowed to depend on the answers received in previous rounds. We show that the capacity of multiround PIR is the same as the capacity of single-round PIR. The result is generalized to also include $T$ -privacy constraints. Combined with previous results, this shows that there is no capacity advantage from multiround over single-round schemes, non-linear over linear schemes or from $\epsilon $ -error over zero-error schemes. However, we show through an example that there is an advantage in terms of storage overhead. We provide an example of a multiround, non-linear, $\epsilon $ -error PIR scheme that requires a strictly smaller storage overhead than the best possible with single-round, linear, zero-error PIR schemes. [ABSTRACT FROM AUTHOR]

Although the ability to collect, collate, and analyze the vast amount of data generated from cyber-physical systems and Internet of Things devices can be beneficial to both users and industry, this process has led to a number of challenges, including privacy and scalability issues. The authors present a hybrid framework where user-centered edge devices and resources can complement the cloud for providing privacy-aware, accurate, and efficient analytics. [ABSTRACT FROM AUTHOR]

Internet-connected toys provide an often-overlooked avenue for breaching personal data, especially of those most vulnerable. Government and private measures can minimize the risks, but responsibility for monitoring smart toy usage ultimately lies with parents. [ABSTRACT FROM AUTHOR]

The Equifax data breach has exposed nearly half of the US adult population to identity theft, but that’s not the real story. [ABSTRACT FROM PUBLISHER]

How can we help users balance the benefits and risks of information disclosure online? A user-tailored privacy approach makes privacy decisions less burdensome by giving users the right kind of information and the right amount of control so as to be useful but not overwhelming or misleading. [ABSTRACT FROM PUBLISHER]