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.     

Trust trumps concern: findings from a seven-country study on consumer consent to ‘digital native’ vs. ‘digital immigrant’ service providers

Many service providers (e.g. mobile network operators) want to turn their business into fully digitalised regimes and offer personalised services. To do so they need access to the consumers’ personal data and must explicitly ask consumers for consent. In this cross-national study, including representative samples from seven countries covering Scandinavia, Central-Eastern Europe and South-Eastern Asia, we investigate the effect of trust vs. privacy concerns and the moderating impact of culture when consents are requested. The study shows that trust in service provider has stronger impact on consent giving than privacy concerns, and indicates that ‘digital native’ more than ‘digital immigrant’ providers should focus on trust building activities when they enter countries with an individualistic culture. The analyses were made within a PLS-SEM framework.     

Can privacy concerns for insurance of connected cars be compensated?

Internet-of-things technologies enable service providers such as insurance companies to collect vast amounts of privacy-sensitive data on car drivers. This paper studies whether and how privacy concerns of car owners can be compensated by offering monetary benefits. We study the case of usage based car insurance services for which the insurance fee is adapted to measured mileage and driving behaviour. A conjoint experiment shows that consumers prefer their current insurance products to usage based car insurance. However, when offered a minor financial compensation, they are willing to give up their privacy to car insurers. Consumers find privacy of behaviour and action more valuable than privacy of location and space. The study is a first to compare different forms of privacy in the acceptance of connected car services. Hereby, we contribute to more fine-grained understanding of privacy concerns in the acceptance of digital services, which will become more important in the upcoming Internet-of-things era.     

Der ökonomische Einfluss von Privacyverletzungen und Securityvorfällen

Privacy and security incidents represent a serious threat for a company’s business success. While previous research in this area mainly investigated second-order effects (e.g., capital market reactions to privacy or security incidents), this study focuses on first-order effects, that is, the direct consumer reaction. In a laboratory experiment, the authors distinguish between the impact of privacy violations and security breaches on the subjects’ trust and behavior. They provide evidence for the so-called “privacy paradox” which describes that people’s intentions, with regard to privacy, differ from their actual behavior. While privacy is of prime importance for building trust, the actual behavior is affected less and customers value security higher when it comes to actual decision making. According to the results, consumers’ privacy related intention-behavior gap persists after the privacy breach occurred.     

A semi-supervised privacy-preserving clustering algorithm for healthcare

With the proliferation of healthcare data, the cloud mining technology for E-health services and applications has become a hot research topic. While on the other hand, these rapidly evolving cloud mining technologies and their deployment in healthcare systems also pose potential threats to patient’s data privacy. In order to solve the privacy problem in the cloud mining technique, this paper proposes a semi-supervised privacy-preserving clustering algorithm. By employing a small amount of supervised information, the method first learns a Large Margin Nearest Cluster metric using convex optimization. Then according to the trained metric, the method imposes multiplicative perturbation on the original data, which can change the distribution shape of the original data and thus protect the privacy information as well as ensuring high data usability. The experimental results on the brain fiber dataset provided by the 2009 PBC demonstrated that the proposed method could not only protect data privacy towards secure attacks, but improve the clustering purity.     

Personalization–privacy paradox and consumer conflict with the use of location-based mobile commerce

This study empirically explored consumers’ response to the personalization–privacy paradox arising from the use of location-based mobile commerce (LBMC) and investigated the factors affecting consumers’ psychological and behavioral reactions to the paradox. A self-administered online consumer survey was conducted using a South Korean sample comprising those with experience using LBMC, and data from 517 respondents were analyzed. Using cluster analysis, consumers were categorized into four groups according to their responses regarding perceived personalization benefits and privacy risks: indifferent (n = 87), personalization oriented (n = 113), privacy oriented (n = 152), and ambivalent (n = 165). The results revealed significant differences across consumer groups in the antecedents and outcomes of the personalization–privacy paradox. Multiple regression analysis showed that factors influence the two outcome variables of the personalization–privacy paradox: internal conflict (psychological outcome) and continued use intention of LBMC (behavioral outcome). In conclusion, this study showed that consumer involvement, self-efficacy, and technology optimism significantly affected both outcome variables, whereas technology insecurity influenced internal conflict, and consumer trust influenced continued use intention. This study contributes to the current literature and provides practical implications for marketers and retailers aiming to succeed in the mobile commerce environment.     

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.     

位置服务中用户轨迹的隐私度量

针对一种流行的用户轨迹隐私保护方法——Silent Cascade,提出一种新的轨迹隐私度量方法.该度量方法将用户运动轨迹用带权无向图描述,并从信息熵的角度计算用户的轨迹隐私水平.已有文献指出,当攻击者拥有新的背景知识时,任何一种隐私保护方法都会受到隐私威胁.因此,将攻击者的背景知识分级融入到度量方法中,隐私度量的结果由对背景知识的假设和相应的轨迹隐私水平值组成,并提出(KUL(Ki+Ki-).KL(Ki+Ki-))联系规则的方法来描述对背景知识的假设.模拟实验结果表明,此度量方法为移动用户和轨迹隐私保护方法的设计者提供了一个有价值的工具,能够准确地评估在攻击者具有可变背景知识情况下,用户的轨迹隐私水平.     

电子商务中消费者隐私保护问题研究

随着精准化营销和个性化服务的广泛使用,一些市场主体越来越注重对消费者的了解,他们在利益驱使下,在 网上消费者不知情或不情愿的情况下采取各种技术手段获得和利用其信息,侵犯了消费者的隐私权。尽管消费者可以因此享 受到更加便利和个性化的产品与服务,但是他们对个人隐私的保护问题也日益关注。本文对电子商务中消费者的隐私权进行 概述,并总结了电子商务中消费者信息收集的常见途径,最后对基于隐私保护的数据挖掘技术与常见算法进行综述。     

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.     

Revealing the relationship between rational fatalism and the online privacy paradox

Previous research has revealed the privacy paradox, which suggests that despite concern about their online privacy, people still reveal a large amount of personal information and don’t take measures to protect personal privacy online. Using data from a national-wide survey, this study takes a psychological approach and uses the rational fatalism theory to explain the privacy paradox on the Internet and the social networking sites (SNSs). The rational fatalism theory argues that risks will become rational if the person believes he or she has no control over the outcome. Our results support the rational fatalism view. We found that people with higher levels of fatalistic belief about technologies and business are less likely to protect their privacy on the Internet in general, and the SNS in particular. Moreover, such relationship is stronger among young Internet users compared with older users.     

Constraint-based and dedication-based mechanisms for encouraging online self-disclosure: Is personalization the only thing that matters?

Consumer-generated self-disclosure is better than firm-generated advertising and sales reports in increasing contact opportunities and also more credible for firms to foster alignment with future market expectations. Previous research mostly assesses online self-disclosure from the rational approach of anticipated benefits and privacy risks without considering the “privacy paradox” phenomenon (users behave contrarily to privacy concern) in social networking sites (SNSs). We develop a theoretical model, grounded in constraint-based (lock-in) and dedication-based (trust-building) mechanisms and social identity theory, to predict online self-disclosure. We test the proposed theoretical model by surveying 395 consumers with participation experience in an online SNS. Different from the rational approach behind personalization, we advance knowledge on how to apply social identity, as well as constraint-based and dedication-based mechanisms, to motivate online self-disclosure induced by consumers. We provide theoretical and practical insights based on our research findings for managing the motivational mechanisms of online self-disclosure.     

Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior

The use of mobile applications continues to experience exponential growth. Using mobile apps typically requires the disclosure of location data, which often accompanies requests for various other forms of private information. Existing research on information privacy has implied that consumers are willing to accept privacy risks for relatively negligible benefits, and the offerings of mobile apps based on location-based services (LBS) appear to be no different. However, until now, researchers have struggled to replicate realistic privacy risks within experimental methodologies designed to manipulate independent variables. Moreover, minimal research has successfully captured actual information disclosure over mobile devices based on realistic risk perceptions. The purpose of this study is to propose and test a more realistic experimental methodology designed to replicate real perceptions of privacy risk and capture the effects of actual information disclosure decisions. As with prior research, this study employs a theoretical lens based on privacy calculus. However, we draw more detailed and valid conclusions due to our use of improved methodological rigor. We report the results of a controlled experiment involving consumers (n=1025) in a range of ages, levels of education, and employment experience. Based on our methodology, we find that only a weak, albeit significant, relationship exists between information disclosure intentions and actual disclosure. In addition, this relationship is heavily moderated by the consumer practice of disclosing false data. We conclude by discussing the contributions of our methodology and the possibilities for extending it for additional mobile privacy research.     

Strategic signaling through cloud service certifications: Comparing the relative importance of certifications’ assurances to companies and consumers

Cloud service certifications (CSCs) are assessed by practitioners to support strategic cloud adoption decisions with the aim to reduce information asymmetries. Both businesses and consumers scrutinize CSCs’ assurances as ex ante signals indicating a cloud provider’s future service quality. While some research has examined the aggregate effects of certifications on decision variables, recipients’ evaluations of certifications and their assurances before making IT-related decisions have received little attention. Furthermore, prior research has predominantly focused on privacy and security assurances in e-commerce certifications. Drawing on signaling theory, we propose that certifications are signals that recipients decompose into a set of fine-grained assurance signals that they weigh to evaluate certifications. We evaluate the responses of 113 company representatives and 317 consumers to a best-worst scaling survey to examine the relative importance these two groups attach to ten assurances from CSCs. Our results show that similar to other online contexts, security and privacy are important assurances, but additional assurances related to availability, the customer friendliness of contracts, and legal compliance are also demanded, particularly by companies. Privacy, security, and availability are most crucial to both companies and consumers, but their relative importance varies substantially between the two groups. Post-hoc subgroup analyses reveal significant differences in assurances’ relative importance for provider and user companies, adopter and non-adopter consumers as well as companies using different types of services and from different industries. Our findings indicate that recipients evaluate certifications as a bundle of signals with varying importance due to recipients’ characteristics and context. With this conceptualization, we contribute to an advanced understanding of the sense-making of certifications and lay out how it influences cloud service adoption theories. Our study has practical implications for certification authorities that design CSCs as well as for providing insights to cloud service providers on customers who draw on CSC assurances when making cloud service adoption decisions.     

Antecedents and outcomes of information privacy concerns in a peer context: An exploratory study

Academic studies typically view privacy threats as originating solely from organizations. With the rise of social media, such a view is incomplete because consumers increasingly face risks from peers’ misuse of data. In this paper, we study information privacy in the context of peer relationships on commercial social media sites. We develop a model that considers relationships between the constructs of privacy experiences, privacy awareness, trust, risk, and benefits and how those relationships impact individuals’ disclosure behaviors. We test the model by creating a survey that includes a number of measures that were taken directly from or were closely based on measures from prior studies. We conduct seven pilot tests of undergraduate students in order to validate the survey items. Working with the online survey firm Qualtrics, we gather a dataset of 314 Facebook users’ responses to our validated survey, and we test our model using partial least squares techniques. We find that both privacy experiences and privacy awareness are quite significant predictors of privacy concerns. We also find that trust, risk, benefits, and privacy concerns work together to explain a large amount (37%) of the variance in disclosure behaviors. We discuss implications for practice and for future research.     

面向移动社交网络的位置隐私保护方法

移动社交网络为人们的生活带来了极大的便利,但用户在享受这些服务带来便利的同时,个人位置隐私受到了严重威胁。首先对用户位置隐私保护需求进行了形式化描述,继而针对用户的敏感兴趣点泄露问题,提出了一种情景感知的隐私保护方法。该方法将位置信息、社交关系、个人信息引入到知识构建算法中以计算兴趣点间的相关性,并利用该相关性及时空情景实时判断发布当前位置是否会泄露用户隐私,进而实现了隐私保护与服务可用性间的平衡。最后通过仿真实验验证了该方法的有效性。     

基于图论与互信息量的差分隐私度量模型

差分隐私是数据发布、数据挖掘领域内隐私保护的重要工具,但其强度和效果仅能后验评估,且高度依赖于经验性选择的隐私预算。文中提出一种基于图论和互信息量的差分隐私量化模型和隐私泄露量计算方法。利用信息论通信模型重构了差分隐私保护框架,构造了差分隐私信息通信模型和隐私度量模型;基于图的距离正则和点传递提出隐私泄露互信息量化方法,证明并计算了差分隐私泄露量的信息量上界。分析和对比表明,该隐私泄露上界与原始数据集的属性数量、属性值数量以及隐私预算参数具有较好的函数关系,且计算限制条件较少。文中所提方法优于现有方法,能够为差分隐私算法的设计及评价、隐私泄露风险评估提供理论支撑。     

Minimal privacy authorization in web services collaboration

With the popularity of Internet technology, web services are becoming the most promising paradigm for distributed computing. This increased use of web services has meant that more and more personal information of consumers is being shared with web service providers, leading to the need to guarantee that the private data of consumers are not illegitimate collected, used and disclosed in services collaboration. This paper studies how to realize the minimal privacy authorization while achieving the functional goals. Initially, this paper uses authorization policies to specify the privacy privileges of the services collaboration, and utilizes the trust relationships among services to make authorization decision. Next, it models the interface behaviors of services by extending the interface automata to support privacy semantics. Furthermore, it quantitatively analyzes the minimum set of privacy privileges which are required by the services to achieve the functional goals, and presents the minimal authorization algorithm, which helps us to automatically derive optimal authorization policies for a services collaboration. Finally, it verifies the correctness and efficiency of the approach proposed by this paper through a case study.     

Inferring Personal Information from Demand-Response Systems

Current and upcoming demand-response systems provide increasingly detailed power-consumption data to utilities and a growing array of players angling to assist consumers in understanding and managing their energy use. The granularity of this data, as well as new players' entry into the energy market, creates new privacy concerns. The detailed per-household consumption data that advanced metering systems generate reveals information about in-home activities that such players can mine and combine with other readily available information to discover more about occupants' activities. The authors explore the technological aspects of this claim, focusing on the ways in which personally identifying information can be collected and repurposed. Their results show that, even with relatively unsophisticated hardware and data-extraction algorithms, some information about occupant behavior can be estimated with a high degree of accuracy. The authors propose a disclosure metric to aid in quantifying the impact of data collection on in-home privacy and construct an example metric for their experiment.     

Understanding consumers’ continuance intention towards mobile purchase: A theoretical framework and empirical study – A case of China

Although mobile purchase is convenient in the age of mobile commerce, many consumers still do not utilize mobile purchase to its full potential. From the mobile vendor’s perspective, retaining current customers and facilitating their continued purchase are crucial to create profitability and achieve a sustainable development. An understanding of the continuance intention towards mobile purchase can provide insights into mobile vendors’ marketing campaigns. Therefore, it is important to examine the determinants that impact continuance intentions of consumers for mobile purchase. Drawing upon information success model, flow theory and trust, this study proposed and empirically tested an integrated model to better understand the determinants of consumers’ continued intention to purchase on mobile sites. Empirical data from 462 users who had experience with mobile purchase were tested against the proposed research model by using structural equation modelling (SEM). The results indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow. System quality, and privacy and security concerns affect satisfaction. Trust affects flow, which in turn affects satisfaction. These three factors together affect continued intention towards mobile purchase. The findings of this study provide several important implications for mobile commerce research and practice.