In Automatic We Trust: Investigating the Impact of Trust,Control, Personality Characteristics,and Extrinsic and Intrinsic Motivations on the Acceptance of Autonomous Vehicles

ABSTRACT

According to industry research, the automation of vehicles promises a revolution in traffic safety, mobility, and quality of life. However, the success of such vehicles depends on their acceptance. This study investigates the influence of trust in technology, concerns of giving up control, perceived usefulness, perceived ease of use, the personality factor innovativeness, and the enjoyment of driving a car on the a priori intention to adopt an autonomous vehicle. By means of an online survey with 369 German participants, our study shows that trust in the technology and the concern about handing over control to a machine go hand in hand as respondents’ cognitive and affective perception of this innovation. Moreover, perceived usefulness represents an influential factor, while the enjoyment of driving a car is a barrier to the technology’s acceptance. Innovators represent a promising target for campaigns, as they are more likely to adopt an autonomous vehicle.     

Willing to pay for quality personalization? Trade-off between quality and privacy

Online personalization presents recommendations of products and services based on customers’ past online purchases or browsing behavior. Personalization applications reduce information overload and provide value-added services. However, their adoption is hindered by customers’ concerns about information privacy. This paper reports on research undertaken to determine whether a high-quality recommendation service will encourage customers to use online personalization. We collected data through a series of online experiments to examine the impacts of privacy and quality on personalization usage and on users’ willingness to pay and to disclose information when using news and financial services. Our findings suggest that under certain circumstances, perceived personalization quality can outweigh the impact of privacy concerns. This implies that service providers can improve the perceived quality of personalization services being offered in order to offset customer privacy concerns. Nevertheless, the impact of perceived quality on personalization usage is weaker for customers who have experienced privacy invasion in the past. The results show that customers who are likely to use online personalization are also likely to pay for the service. This finding suggests that, despite privacy concerns, there is an opportunity for businesses to monetize high-quality personalization.     

The effects of privacy concerns and personal innovativeness on potential and experienced customers’ adoption of location-based services

Location-Based Services (LBS) use positioning technology to provide individual users the capability of being constantly reachable and accessing network services while ‘on the move’. However, privacy concerns associated with the use of LBS may ultimately prevent consumers from gaining the convenience of ‘anytime anywhere’ personalized services. We examine the adoption of this emerging technology through a privacy lens. Drawing on the privacy literature and theories of technology adoption, we use a survey approach to develop and test a conceptual model to explore the effects of privacy concerns and personal innovativeness on customers’ adoption of LBS. In addition, as a number of IS researchers have shown that customers differ in their decision making for continued adoption as compared to initial decision making, we test the research model separately for potential and experienced customers. The results indicate that privacy concerns significantly influence continued adoption as compared to initial adoption. The implications for theory and practice are discussed.     

Accident responsibility identification model for Internet of Vehicles based on lightweight blockchain

The rapid development of autonomous vehicle technology has brought a new experience to people's daily travel. However, if a traffic accident involving autonomous vehicles occurs, it will face difficulties in vehicle accident forensic-preservation, leakage of vehicle owner's privacy, and identifying legal liabilities. This article proposes an accident responsibility identification model for the Internet of Vehicles based on lightweight blockchain to solve the above problems. This model uses Car Forensics Master to collect evidence from the accident vehicle, and at the same time collects evidence from maintenance service providers, automobile manufacturers, transportation management departments, insurance companies, and other vehicle accident related parties and stores them in the preservation chain. We also use VPKI to protect the autonomous vehicle identity privacy. In order to improve the efficiency of the model and set up authorized access to related entities, the identification of accident liability is jointly completed by the preservation chain and the accident identification chain. In addition, we prove that the protocol proposed in the model has ideal security properties. Finally, we implement the smart contracts in the model through the Solidity language, and evaluate its performance.     

车联网隐私保护技术研究

随着汽车智能化、网联化程度的不断加深,车辆、用户及第三方机构之间的数据共享日益成为刚需,由车辆、用户、路边单元等通信实体之间构建的网络车联网应运而生,而车联网的高移动性和网络拓扑多变性使其更容易遭受攻击,进而导致严重的车联网用户隐私泄露问题。如何平衡数据共享和隐私保护之间的关系成为车联网产业发展所面临的一个关键挑战。近年来,学术界针对车联网隐私保护问题进行了深入的研究,并提出了一系列解决方案,然而,目前缺少对这些方案从隐私属性方面进行分析。为此,本文首先从车联网的系统架构、通信场景及标准进行阐述。然后对车联网隐私保护的需求、攻击模型及隐私度量方法进行分析与总结。在此基础上从车联网身份隐私、匿名认证位置隐私和车联网位置服务隐私三个方面出发,介绍了匿名认证、假名变更、同态加密、不经意传输等技术对保护车联网用户隐私起到的重要作用,并讨论了方案的基本原理及代表性实现方法,将方案的隐私性从不可链接性、假名性、匿名性、不可检测性、不可观察性几个方面进行了分析与总结。最后探讨了车联网隐私保护技术当前面临的挑战及进一步研究方向,并提出了去中心化的车辆身份隐私技术以保护车辆身份隐私、自适应假名变更技术以支持匿名认证、满足个性化隐私需求的位置服务隐私保护技术,以期望进一步推动车联网隐私保护技术研究的发展与应用。     

Deep Packet Inspection: Shaping the Internet and the Implications on Privacy and Security

ABSTRACT

In recent debates on the issues of privacy and the Internet, Deep Packet Inspection (DPI) has been grossly oversimplified as a technology that is primarily harmful to consumers. Much of the commentary has been based on a poor understanding of what DPI is and how DPI works. All too often the debate over DPI is focused on unrelated issues such as free speech and censorship, which are largely political and not technological issues. DPI usage has been described as a violation of consumer privacy when the reality is far more complex and nuanced. What has been missing is a broader discussion on the full nature and application of DPI technology; DPI enables a wide range of applications, most of which are not only positive but also essential to the survival of the Internet. This paper will explain how DPI technology works and explore practical applications of its use. DPI will be an important tool to control economies of scale with the explosive growth of the Internet. While there will always be privacy concerns over the intrusive nature of DPI technology, this worry will be overshadowed by the security and control it allows.     

Investigating user activities and the corresponding requirements for information and functions in autonomous vehicles of the future

The potential benefits of autonomous vehicles, including safety, convenience, fuel economy, and low emissions can only be achieved when consumers are comfortable with the vehicle design. There are only a limited number of user studies in the design of future autonomous vehicles, owing to the difficulties of shifting focus “from the present to the future.” An integrated method of simulator study and user enactment was applied in the research to bridge the gap between the current and the future. Thirty drivers participated in the study to experience enacted driving scenarios in an autonomous vehicle simulator. The participants were divided into two groups, i.e., driving-alone drivers and driving-with-a-passenger drivers, to investigate the effect of passenger presence. Rich data were elicited about possible in-vehicle activities, the corresponding requirements of information and functions to support any such activities. Also identified were the preferred methods of interacting with the information and functions. Passenger presence was found to have an influence on the attributes of activities undertaken as well as the preferences for in-vehicle information and functions. Dominant themes were identified in future autonomous vehicle designs, including a more flexible and adaptive design language, concerns of trust and safety, and trade-offs between safety and convenience and between privacy and social connection. Based on the findings, design implications for future autonomous vehicles are discussed.     

Privacy debate centers on radio frequency identification

The emergence of radio frequency identification (RFID) has brought with it a plethora of privacy concerns and experts are questioning whether the hoopla surrounding RFID is justified. Using RFID should trigger the same privacy concerns as other commonly used technology such as credit cards, cell phones, and the Internet. RFID's potential to revolutionize the retail industry by maximizing suppliers' ability to control inventory and reduce theft is widely recognized. In fact, some technology forecasters predict that RFID tags will eventually replace bar codes on almost all product packaging. The privacy debate centers around RFID tags themselves, which function like tiny radios, wirelessly transmitting information to network receivers. If RFID tags were to remain active even after consumers complete their purchases and exit stores, their wireless technology would let the stores track consumers' movement and behavior; or so goes the argument.     

On the road – listening to data subjects’ personal mobility data privacy concerns

Efficient utilisation of new mobility data-based services and promotion of acceptance of data collection from vehicles and people demand an understanding of mobility data privacy concerns, associated with increasing use of tracking technologies, diverse data usages and complex data collection environments. Understanding privacy concerns enables improved service and system development and identification of appropriate data management solutions that contribute to data subjects’ privacy protection, as well as efficient utilisation of the collected data. This study aimed to explore earlier research findings on privacy concerns evaluation and investigate their validity in mobility data collection. Explorative multimethod research was conducted in a mobility service pilot through data controller interviews, user interviews and a user survey. The study's results indicated the need to revise and complement existing privacy concerns evaluation in mobility data collection contexts. The primary findings were as follows: (1) Privacy concerns specific to the mobility data collection context exist. (2) Privacy concerns may change during the service use. (3) Users are not necessarily personally worried about their privacy although they ponder on privacy issues. (4) In contrast to traditional ‘privacy calculus’ thinking, users’ expected benefits from data disclosure may also be driven by altruistic motives.     

Playing Fair with Consumer Privacy in the Global On-line Environment

The advent of global on-line shopping has been heralded in glowing terms for the consumer—more choice, keener prices and you don't have to leave your living room to shop. But behind such adulation, there lies a darker side to this new panacea, and without appropriate controls is a hidden intangible cost that many see as outweighing the trumpeted benefits of consumer e-commerce, that being the threat to personal data privacy due to the unprecedented potential for data collection and manipulation that are facilitated by the new medium. This paper will look behind the gloss of e-commerce, to analyse the seriousness of this threat. We will specifically consider whether this new electronic environment is such, that existing data privacy protection provisions are inadequate in meeting the challenge posed by the architecture of on-line sites and the computing power they possess. A main feature of our discussion will be a consideration of the problems created in this global environment, where jurisdictions have differing levels of privacy protection, this being specifically pertinent to the European Union and the United States, who have been involved in protracted negotiations on transborder data privacy protection due to the stronger privacy protection afforded by the EU Data Protection Directive,1 than that provided for by the United States.     

Dynamic mix zone: location data sanitizing in assisted environments

Pervasive technology has been widely used in assistive environments and aware homes. The issue of how to preserve the privacy of patients being monitored has been attracting more public concerns. In assistive environments, location data of patients are collected through sensors for behavior patterns analysis, and they can also be shared among researchers for further research for early disease diagnosis. However, location information, even though de-identified, also introduces the risk of privacy leakage. A series of consecutive location samples can be considered as a trajectory of a single person, and this may leak private information if obtained by malicious users. This paper discusses this problem and proposes a location randomization algorithm to protect users’ location privacy. Two privacy metrics according to location privacy are defined and used to evaluate the proposed approach. A method using dynamic mix zones is proposed to confound trajectories of two or more persons.     

Predicting user response to sponsored advertising on social media via the technology acceptance model

Sponsored advertising has generated strong advertising revenues for Facebook in recent years. As sponsored ads are built on an interactive platform that could be seen as invasive to user privacy, the growth of this advertising platform has important implications for consumers, and advertisers alike. As little research is available on consumer response to sponsored advertising as an interactive technology innovation, the current study assesses the effects of user perceptions of privacy risk, intrusiveness concerns and utilities of sponsored advertising on consumer attitudes and purchase intent. Testing a model derived form the technology acceptance model (TAM), the study found that privacy and intrusiveness concerns are both valid antecedent variables to perceived usefulness but not perceived ease of use of sponsored advertising. While both antecedent variables also influence consumer attitudes toward sponsored advertising, only privacy concerns have an impact product purchase intentions. The hypothesized relations between perceived usefulness, ease of use, attitudes and purchase intentions were also validated.     

Digital steganography: hiding data within data

Digital steganography is the art of inconspicuously hiding data within data. Steganography's goal in general is to hide data well enough that unintended recipients do not suspect the steganographic medium of containing hidden data. The software and links mentioned in this article are just a sample of the steganography tools currently available. As privacy concerns continue to develop along with the digital communication domain, steganography will undoubtedly play a growing role in society. For this reason, it is important that we are aware of digital steganography technology and its implications. Equally important are the ethical concerns of using steganography and steganalysis. Steganography enhances rather than replaces encryption. Messages are not secure simply by virtue of being hidden. Likewise, steganography is not about keeping your message from being known - it's about keeping its existence from being known     

基于车联网的合谋攻击研究

车联网系统中的Sybil攻击检测方案大多使用匿名认证来保障车辆隐私安全,但这种认证方式在面对恶意车辆之间合谋发动Sybil攻击时将不具有有效性.本文阐述了现有的车联网身份认证技术中存在合谋攻击,并提出了一种较为有效的检测方案.     

Privacy and biometrics: An empirical examination of employee concerns

Advances in authentication technology have led to a proliferation of biometric-based systems in the workplace. Although biometric technologies offer organizations a cost-effective method of increasing security, employees are often hesitant to permit use. The collection and storage of employee biometric data raises concerns about proper use of these intensely personal identifiers. This work draws from organizational privacy practices, electronic monitoring, procedural fairness, self-construal, and technology adoption theories. We investigate the effects of independent and interdependent self-construal on three newly developed dimensions of employee privacy concern related to organizational use of biometric technology. These dimensions include perceived accountability, perceived vulnerability, and perceived distrust toward the organization. We test the predictive power of our model using data from an organization deploying a new biometric system designed to track employee work assignments under the auspices of improving personnel safety. Results indicate that self-construal plays a significant role in the formulation of privacy concerns and both perceived accountability concerns and perceived vulnerability concerns are significant predictors of attitude toward using biometric technology in the workplace.     

RFID系统中安全和隐私问题的研究

射频识别（RFID）技术的应用将给零售、物流等产业带来革命性的变化,但同时出现的安全和隐私问题却极大地限制了其使用的范围,如何在RFID系统中保证安全、维护个人隐私成为业界讨论的热点问题。在分析四种常见解决方案的基础上,阐明了各自的优缺点,探讨了在RFID系统中解决安全和隐私问题所面临的挑战,并指出了其发展的方向。     

A survey on solutions to support developers in privacy-preserving IoT development

Internet-of-Things (IoT) devices are rising in popularity and their usefulness often stems from the amount of data they collect. Data regulations such as the European General Data Protection Regulation (GDPR) require software developers to do their due diligence when it comes to privacy, as they are required to adhere to certain principles such as Privacy-by-Design (PbD). Due to the distributed and heterogeneous nature of IoT applications, privacy-preserving design is even more important in IoT environments. Studies have shown that developers are often not eager to implement privacy and generally do not see it as their duty or concern. However, developers are often left alone when it comes to engineering privacy in the realm of IoT. In this paper, we therefore survey which frameworks and tools have been developed for them, especially in the case of IoT. Our findings indicate that existing solutions are cumbersome to use, only work in certain scenarios, and are not enough to solve the privacy issues inherent IoT development. Based on our analysis, we further propose future research directions.     

An analysis of privacy signals on the World Wide Web: Past, present and future

Electronic commerce is now an established, vital part of the world economy. However, this economic sector is currently endangered by consumers’ well-founded concern for the privacy of their information. Recent surveys indicate that this concern is beginning to alter consumers’ spending habits. The World Wide Web Consortium is well aware of these concerns, and has produced the Platform for Privacy Preferences Protocol as a mechanism to help consumers protect their online privacy. This mechanism relies on the use of machine-readable privacy policies, posted on a website, and interpreted by a client-side browser extension. However, recent surveys indicate that adoption of this technology is stagnating on the server side, and work on an updated version of the Platform has been halted. We use signaling theory as a framework to model the likely future evolution of the Platform, in an effort to gauge whether it will flourish or wither as a technology. We find that signaling theory predicts the collapse of the Platform for Privacy Preferences Protocol. However, we also find theoretical and empirical grounds to predict that government intervention can drive adoption of the Platform on the server side, which may in turn bootstrap user adoption of this technology.     

AIoT-enabled smart surveillance for personal data digitalization: Contextual personalization-privacy paradox in smart home

Artificial intelligence of things technology provides smart surveillance capability for personal data digitalization. It will invade individuals’ information, physical, and social spaces and raise contextual privacy concerns while providing personalized services, which has not been explored in previous research. We theorize three types of smart surveillance and identify three subdimensions of contextual personalization and privacy concerns. Grounded in surveillance theory and personalization-privacy paradox, we examined the different trade-offs of contextual personalization and privacy concerns underlying the three types of smart surveillance on users’ behavioral intention in smart home context. The results also indicated that transparency can lessen the trade-off effects.     

A Scalable Privacy Preserving Scheme in Vehicular Network

Vehicles enlisted with computing, sensing and communicating devices can create vehicular networks, a subset of cooperative systems in heterogeneous environments, aiming at improving safety and entertainment in traffic. In vehicular networks, a vehicle＇s identity is associated to its owner＇s identity as a unique linkage. Therefore, it is of importance to protect privacy of vehicles from being possibly tracked. Obviously, the privacy protection must be scalable because of the high mobility and large population of vehicles. In this work, we take a non-trivial step towards protecting privacy of vehicles. As privacy draws public concerns, we firstly present privacy implications of operational challenges from the public policy perspective. Additionally, we envision vehicular networks as geographically partitioned subnetworks （cells）. Each subnetwork maintains a list of pseudonyms. Each pseudonym includes the cell＇s geographic id and a random number as host id. Before starting communication, vehicles need to request a pseudonym on demand from pseudonym server. In order to improve utilization of pseudonyms, we address a stochastic model with time-varying arrival and departure rates. Our main contribution includes： 1） proposing a scalable and effective algorithm to protect privacy; 2） providing analytical results of probability, variance and expected number of requests on pseudonym servers. The empirical results confirm the accuracy of our analytical predictions.