The upside of data privacy – delighting customers by implementing data privacy measures

The targeted analysis of customer data becomes increasingly important for data-driven business models. At the same time, the customers’ concerns regarding data privacy have to be addressed properly. Existing research mostly describes data privacy as a necessary evil for compliance and risk management and does not propose specific data privacy measures which address the customers’ concerns. We therefore aim to shed light on the upside of data privacy. In this paper, we derive specific measures to deal with customers’ data privacy concerns based on academic literature, legislative texts, corporate privacy statements, and expert interviews. Next, we leverage the Kano model and data from two internet-based surveys to analyze the measures’ evaluation by customers. From a customer perspective, the implementation of the majority of measures is obligatory as those measures are considered as basic needs of must-be quality. However, delighting measures of attractive quality do exist and have the potential to create a competitive advantage. In this, we find some variation across different industries suggesting that corporations aiming to improve customer satisfaction by superior privacy protection should elicit the demands of their specific target customers.     

Is technology meeting the privacy challenge?

Privacy is one of consumers' most important concerns about using the Internet for e-commerce and other purposes. With this in mind, a number of privacy-related technologies are emerging, including improved software that anonymizes user identities, tools that audit a company's privacy policy adherence, and standards. Technology's effect on privacy has caused concern since commercial computing emerged in the 1960s. However, the Internet has caused people to look at this issue again in a new way, and we have seen a lot more technologies that are invasive, as well as technologies that enhance privacy     

Consensus with preserved privacy against neighbor collusion

This paper proposes a privacy-preserving algorithm to solve the average-consensus problem based on Shamir’s secret sharing scheme, in which a network of agents reach an agreement on their states without exposing their individual states until an agreement is reached. Unlike other methods, the proposed algorithm renders the network resistant to the collusion of any given number of neighbors (even with all neighbors’ colluding). Another virtue of this work is that such a method can protect the network consensus procedure from eavesdropping.     

Information privacy and security — A regulator's priorities

This article discusses current events in the Data Protection field, and discusses the priorities considered important by the UK Data Protection Registrar.     

Data privacy as a tool to combat discrimination?

This article will show how data privacy is legally recognized and treated in Japan and argue in particular how it can (not) play a role as a tool to combat discrimination in the workplace. The first part will depict a brief history of the development of the ‘right to privacy’ in Japan. This part will also take a look at the national Act on Protection of Personal Information and its complicated enforcement, paying attention to the general legal culture in Japan. The second part will observe other laws which are related to personal data protection and legal protection against discrimination. At the end of this article the author will make a comment on the Japanese data privacy law and its desirable development.     

Narrow privacy and desynchronization in Vaudenay’s RFID model

Forward privacy of RFID systems and its relaxed version, narrow forward privacy, are generally considered satisfactory for practical needs. Unfortunately, the attempt to get forward privacy by symmetric-key cryptography failed. Also, all symmetric-key cryptography-based RFID systems proposed so far that are strictly narrow forward private (that is, narrow forward private but not forward private) suffer from desynchronization. Under these circumstances, the question whether the attempt to exceed these limits is doomed to failure or not was frequently asked. The paper we are proposing wants to clarify this matter. Thus, we show that forward privacy in Vaudenay’s RFID model cannot be achieved with symmetric-key cryptography. Then, we show that strictly narrow forward privacy can be achieved with symmetric-key cryptography only by RFID systems with unbounded desynchronization. This last result holds for strictly narrow destructive privacy and strictly narrow strong privacy too, if one wants to achieve them with symmetric-key cryptography.

     

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.     

A privacy problem on Hu–Huang's proxy key generation protocol

A proxy signature scheme enables an original signer to delegate his signing capability to a proxy signer and then the proxy signer can sign a message on behalf of the original signer. Recently, several ID-based proxy signature schemes have been proposed. ID-based Cryptosystems (ID-Cs) are advantageous than the traditional Public Key Cryptosystems (PKCs), as the key distribution is not required. But, an inherent problem of ID-Cs is the key escrow. Hu and Huang also proposed an ID-based proxy signature scheme with proxy key generation protocol. In Hu–Huang's scheme, whenever the original signer requests the Public Key Generation Center (PKG) to generate a proxy derivation key for a designated proxy signer, it generates a proxy key using the proxy signer's private key without his agreement. Thus, the PKG gets to infringe general privacy laws and regulations. Also, this is an example of PKG's misuse of the user's private key due to ID-C's key escrow problem. To solve these problems we propose an improvement of Hu–Huang's scheme.     

Why current differential privacy schemes are inapplicable for correlated data publishing?

World Wide Web - Although data analysis and mining technologies can efficiently provide intelligent and personalized services to us, data owners may not always be willing to share their true data...     

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.     

Comments on “A privacy preserving three-factor authentication protocol for e-health clouds”

The security provisioning of increasing healthcare data is of critical importance. The e-health clouds can be seen as a move towards an efficient management of such a big volume of healthcare data. Many schemes have been presented to bring more security and privacy along with efficiency, in the handling of data for booming e-health industry. Recently, in this connection, Jiang et al. (J Supercomput 1–24 doi: 10.1007/s11227-015-1610-x, 2016) presented a three-factor authentication protocol for e-health clouds. In this letter, we identify a serious flaw in the mutual authentication phase of the scheme, since an adversary may launch a denial-of-service attack (DoS) against the service providing server. Finally, we suggest a modification in the scheme to overcome the DoS attack.     

Technological solutions to privacy questions: what is the role of law?

In spite of its recognition as a fundamental human right, privacy is sometimes criticised as an anachronistic value in modern life [James Rule, Privacy in Peril (OUP, 2007) xi]. While the prominence of this view has lessened in the wake of the Snowden revelations and increased public concern with online privacy [Maria Helen Murphy, ‘The Pendulum Effect: Comparisons Between the Snowden Revelations and the Church Committee. What are the Potential Implications for Europe?’ (2014) 23(2) Information and Communications Technology Law 192], the right continues to struggle for support when it is portrayed as being in competition with national security, personal safety, and economic prosperity [Daniel Solove, ‘“I've Got Nothing to Hide” and other Misunderstandings of Privacy’ (2007) 44 San DLR 745]. As developments in technology continue to threaten the right to privacy, interest in technological solutions to privacy problems continues to grow. This article seeks to consider current privacy debates from the perspectives of multiple stakeholders in order to assess whether technological and design approaches offer the best path forward, or whether an essential role remains to be played by law.     

Shall I post this now? Optimized,delay-based privacy protection in social networks

Despite the several advantages commonly attributed to social networks such as easiness and immediacy to communicate with acquaintances and friends, significant privacy threats provoked by unexperienced or even irresponsible users recklessly publishing sensitive material are also noticeable. Yet, a different, but equally significant privacy risk might arise from social networks profiling the online activity of their users based on the timestamp of the interactions between the former and the latter. In order to thwart this last type of commonly neglected attacks, this paper proposes an optimized deferral mechanism for messages in online social networks. Such solution suggests intelligently delaying certain messages posted by end users in social networks in a way that the observed online activity profile generated by the attacker does not reveal any time-based sensitive information, while preserving the usability of the system. Experimental results as well as a proposed architecture implementing this approach demonstrate the suitability and feasibility of our mechanism.     

Law, privacy and information technology: a sleepwalk through the surveillance society?

The Surveillance Studies Network report of 2006 on the 'surveillance society', highlighting the omnipresence of information technology in British society, once again brought into sharp focus concerns about the types and levels of technological surveillance to which the public are subjected. This article seeks to explore the opportunities for surveillance presented by recent developments, and suggests a number of privacy and civil liberties concerns.     

On the privacy offered by (k, δ)-anonymity

The widespread deployment of technologies with tracking capabilities, like GPS, GSM, RFID and on-line social networks, allows mass collection of spatio-temporal data about their users. As a consequence, several methods aimed at anonymizing spatio-temporal data before their publication have been proposed in recent years. Such methods are based on a number of underlying privacy models. Among these models, (k,δ)-anonymity$(k,\delta )\mathrm{-}\mathrm{anonymity}$ claims to extend the widely used k  -anonymity concept by exploiting the spatial uncertainty δ≥0$\delta \ge 0$ in the trajectory recording process. In this paper, we prove that, for any δ>0$\delta >0$ (that is, whenever there is actual uncertainty), (k,δ)-anonymity$(k,\delta )\mathrm{-}\mathrm{anonymity}$ does not offer trajectory k-anonymity, that is, it does not hide an original trajectory in a set of k   indistinguishable anonymized trajectories. Hence, the methods based on (k,δ)-anonymity$(k,\delta )\mathrm{-}\mathrm{anonymity}$, like Never Walk Alone (NWA) and Wait For Me (W4M) can offer trajectory k  -anonymity only when δ=0$\delta =0$ (no uncertainty). Thus, the idea of exploiting the recording uncertainty δ$\delta$ to achieve trajectory k  -anonymity with information loss inversely proportional to δ$\delta$ turns out to be flawed.     

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.     

Understanding and capturing people’s privacy policies in a mobile social networking application

A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people’s attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or “policies”). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies.

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Beyond concern: a privacy–trust–behavioral intention model of electronic commerce

Despite the recent economic downturn in the Internet and telecommunication sectors, electronic commerce (EC) will continue to grow and corporate Web sites will remain an important communication channel. However, legitimate concerns regarding privacy and trust remain potential obstacles to growth and important issues to both individuals and organizations. This study proposed and tested a theoretical model that considers an individual’s perceptions of privacy and how it relates to his or her behavioral intention to make an online transaction. An experiment that included over 200 subjects was conducted using two EC sites that differed only by the privacy dimensions of their notice, access, choice, and security. The results of this study suggested strong support for the model.