Research and Development of a Cyber-I Open Service Platform

研究成果の概要 (和文) : Cyber-Iは、Real-Iのデジタル対応であり、個人データの収集と分析を行い、人の行動や感情に近づけます。本研究では、複数のデバイスから多くの個人データを収集して処理を行い、Cyber-Iの作成と管理を行うようなオープンサービスプラットフォームを開発しました。異なるデバイスやデータを柔軟でスケーラブルな管理をするために、スマートフォンをゲートウェイとして使用するクラウドやフォグベースのデータベースシステムを実装しています。また、Cyber-Iの成長をコントロールするような基本的技術やメカニズムを提案しています。さらにCyber-I関連における個人情報の保護と利用についても検討しています。研究成果の概要 (英文) : Cyber-I, short for Cyber Individual, is a digital counterpart of Real-Individual (Real-I), and is expected to continuously approximate a real person’s behavior and even mind with collections and analyses of increasing personal data. In this research, a Cyber-I open service platform has been researched and developed to collect and process rich personal big data from various sources and multiple devices for Cyber-I creation and administration as well as its modeling and life control. A cloud-fog based database system using smartphones as gateways has been implemented for flexible and scalable managements of heterogeneous devices and data. Basic strategy and mechanism have been proposed for scheduling and controlling Cyber-I growth. Cyber-I related data privacy protection and personal information usage are also studied. A series of researches on personality and affective computing has been carried out to model personal characteristics

Privacy in the Sharing Economy

Contemporary C2C platforms, such as Airbnb, have exhibited considerable growth in recent years and are projected to continue doing so in the future. These novel consumer-to-consumer marketplaces have started to obliterate the boundaries between private and economic spheres. Marketing personal resources online is inherently associated with the disclosure of personal and sometimes intimate information. This raises unprecedented questions of privacy. Yet, there is so far little research on the role of privacy considerations in the sharing economy literature. Leveraging the theoretical perspective of privacy calculus, we address this gap by investigating how privacy concerns and economic prospects shape a potential provider’s intentions to share via different communication channels. We relate privacy concerns back to the provider’s perceptions of the audience. We evaluate our research model by means of a scenario-based online survey, providing broad support for our reasoning

Combating User Misbehavior on Social Media

Social media encourages user participation and facilitates user’s self-expression like never before. While enriching user behavior in a spectrum of means, many social media platforms have become breeding grounds for user misbehavior. In this dissertation we focus on understanding and combating three specific threads of user misbehaviors that widely exist on social media — spamming, manipulation, and distortion. First, we address the challenge of detecting spam links. Rather than rely on traditional blacklist-based or content-based methods, we examine the behavioral factors of both who is posting the link and who is clicking on the link. The core intuition is that these behavioral signals may be more difficult to manipulate than traditional signals. We find that this purely behavioral approach can achieve good performance for robust behavior-based spam link detection. Next, we deal with uncovering manipulated behavior of link sharing. We propose a four-phase approach to model, identify, characterize, and classify organic and organized groups who engage in link sharing. The key motivating insight is that group-level behavioral signals can distinguish manipulated user groups. We find that levels of organized behavior vary by link type and that the proposed approach achieves good performance measured by commonly-used metrics. Finally, we investigate a particular distortion behavior: making bullshit (BS) statements on social media. We explore the factors impacting the perception of BS and what leads users to ultimately perceive and call a post BS. We begin by preparing a crowdsourced collection of real social media posts that have been called BS. We then build a classification model that can determine what posts are more likely to be called BS. Our experiments suggest our classifier has the potential of leveraging linguistic cues for detecting social media posts that are likely to be called BS. We complement these three studies with a cross-cutting investigation of learning user topical profiles, which can shed light into what subjects each user is associated with, which can benefit the understanding of the connection between user and misbehavior. Concretely, we propose a unified model for learning user topical profiles that simultaneously considers multiple footprints and we show how these footprints can be embedded in a generalized optimization framework. Through extensive experiments on millions of real social media posts, we find our proposed models can effectively combat user misbehavior on social media

Protecting attributes and contents in online social networks

With the extreme popularity of online social networks, security and privacy issues become critical. In particular, it is important to protect user privacy without preventing them from normal socialization. User privacy in the context of data publishing and structural re-identification attacks has been well studied. However, protection of attributes and data content was mostly neglected in the research community. While social network data is rarely published, billions of messages are shared in various social networks on a daily basis. Therefore, it is more important to protect attributes and textual content in social networks. We first study the vulnerabilities of user attributes and contents, in particular, the identifiability of the users when the adversary learns a small piece of information about the target. We have presented two attribute-reidentification attacks that exploit information retrieval and web search techniques. We have shown that large portions of users with online presence are very identifiable, even with a small piece of seed information, and the seed information could be inaccurate. To protect user attributes and content, we adopt the social circle model derived from the concepts of "privacy as user perception" and "information boundary". Users will have different social circles, and share different information in different circles. We introduce a social circle discovery approach using multi-view clustering. We present our observations on the key features of social circles, including friendship links, content similarity and social interactions. We treat each feature as one view, and propose a one-side co-trained spectral clustering technique, which is tailored for the sparse nature of our data. We also propose two evaluation measurements. One is based on the quantitative measure of similarity ratio, while the other employs human evaluators to examine pairs of users, who are selected by the max-risk active evaluation approach. We evaluate our approach on ego networks of twitter users, and present our clustering results. We also compare our proposed clustering technique with single-view clustering and original co-trained spectral clustering techniques. Our results show that multi-view clustering is more accurate for social circle detection; and our proposed approach gains significantly higher similarity ratio than the original multi-view clustering approach. In addition, we build a proof-of-concept implementation of automatic circle detection and recommendation methods. For a user, the system will return its circle detection result from our proposed multi-view clustering technique, and the key words for each circle are also presented. Users can also enter a message they want to post, and the system will suggest which circle to disseminate the message

The enchanted house:An analysis of the interaction of intelligent personal home assistants (IPHAs) with the private sphere and its legal protection

Abstract In less than five years, Alexa has become a familiar presence in many households, and even those who do not own one have stumbled into it, be it at a friend’s house or in the news. Amazon Alexa and its friend Google Assistant represent an evolution of IoT: they have an advanced ‘intelligence’ based on Cloud computing and Machine Learning; they collect data and process them to profile and understand users, and they are placed inside our home. I refer to them as intelligent personal and home assistants, or IPHAs.  This research applies multidisciplinary resources to explore the phenomenon of IPHAs from two perspectives. From a more socio-technical angle, the research reflects upon what happens to the private sphere and the home once IPHAs enter it. To do so, it looks at theories and concepts borrowed from history, behavioural science, STSs, philosophy, and behavioural design. All these disciplines contribute to highlight different attributes that individuals and society associate with the private sphere and the home. When the functioning of IPHAs is mapped against these attributes it is possible to identify where Alexa and Assistant might have an impact: there is a potential conflict between the privacy expectations and norms existing in the home (as sanctuary of the private sphere) and the marketing interests introduced in the home by IPHAs’ profiling. Because of the voice-interaction, IPHAs are also potentially highly persuasive, can influence and manipulate users and affect their autonomy and control in their daily lives. From the legal perspective, the research explores the application of the GDPR and proposal for e-Privacy Regulation to IPHAs, as legislative tools for the protection of the private sphere in horizontal relationships. The analysis focuses in particular on those provisions whose application to IPHAs is more challenging, based on the technology but also on the sociotechnical analysis above. Special attention is dedicated to the consent of users to the processing, the general principles of the GDPR, attributing the role of controllers or processors to the stakeholders involved, profiling and automated decisions, data protection by design and default, as well as spam and robocalls. For some of the issues, suggestions are offered on how to interpret and apply the legal framework, in order to mitigate undesired effects. This is the case, for instance, of determining whether the owners of IPHAs should be considered controllers vis-à-vis the data of their guests, or of the implications of data protection by design and default on the design of IPHAs. Some questions, however, require a wider debate at societal and political level. This is the case of the behavioural design techniques used to entice users and stimulate them to use the vocal assistants, which present high levels of persuasion and can affect the agency and autonomy of individuals. The research brings forward the necessity to determine where the line should be drawn between acceptable practices and unacceptable ones

Personalizing Human-Robot Dialogue Interactions using Face and Name Recognition

Task-oriented dialogue systems are computer systems that aim to provide an interaction indistinguishable from ordinary human conversation with the goal of completing user- defined tasks. They are achieving this by analyzing the intents of users and choosing respective responses. Recent studies show that by personalizing the conversations with this systems one can positevely affect their perception and long-term acceptance. Personalised social robots have been widely applied in different fields to provide assistance. In this thesis we are working on development of a scientific conference assistant. The goal of this assistant is to provide the conference participants with conference information and inform about the activities for their spare time during conference. Moreover, to increase the engagement with the robot our team has worked on personalizing the human-robot interaction by means of face and name recognition. To achieve this personalisation, first the name recognition ability of available physical robot was improved, next by the concent of the participants their pictures were taken and used for memorization of returning users. As acquiring the consent for personal data storage is not an optimal solution, an alternative method for participants recognition using QR Codes on their badges was developed and compared to pre-trained model in terms of speed. Lastly, the personal details of each participant, as unviversity, country of origin, was acquired prior to conference or during the conversation and used in dialogues. The developed robot, called DAGFINN was displayed at two conferences happened this year in Stavanger, where the first time installment did not involve personalization feature. Hence, we conclude this thesis by discussing the influence of personalisation on dialogues with the robot and participants satisfaction with developed social robot

Data and the city – accessibility and openness. a cybersalon paper on open data

This paper showcases examples of bottom–up open data and smart city applications and identifies lessons for future such efforts. Examples include Changify, a neighbourhood-based platform for residents, businesses, and companies; Open Sensors, which provides APIs to help businesses, startups, and individuals develop applications for the Internet of Things; and Cybersalon’s Hackney Treasures. a location-based mobile app that uses Wikipedia entries geolocated in Hackney borough to map notable local residents. Other experiments with sensors and open data by Cybersalon members include Ilze Black and Nanda Khaorapapong's The Breather, a "breathing" balloon that uses high-end, sophisticated sensors to make air quality visible; and James Moulding's AirPublic, which measures pollution levels. Based on Cybersalon's experience to date, getting data to the people is difficult, circuitous, and slow, requiring an intricate process of leadership, public relations, and perseverance. Although there are myriad tools and initiatives, there is no one solution for the actual transfer of that data