Money walks

Staiano, Jacopo; Oliver, Núria; Lepri, Bruno; Oliveira, Rodrigo de; Caraviello, Michele; Sebe, Nicu

doi:10.1145/2632048.2632074

Cited by 68 publications

(13 citation statements)

References 48 publications

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“…This has become more important of late, as more recent data mining applications involve the analysis of personal data, much of which is collected by individuals and contributed to marketers in what has been termed "self-surveillance" [25]. Such personal data have been demonstrated to be highly valuable [48], and have even been described as the new "oil" in terms of the value of their resource [55]. Value aside, such data introduce new challenges for consent as they can often be combined to create new inferences and profiles where previously data would have been absent [17].…”

Section: Introductionmentioning

confidence: 99%

Beyond the EULA: Improving Consent for Data Mining

Hutton

Henderson

2017

Studies in Big Data

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Companies and academic researchers may collect, process, and distribute large quantities of personal data without the explicit knowledge or consent of the individuals to whom the data pertains. Existing forms of consent often fail to be appropriately readable and ethical oversight of data mining may not be sufficient. This raises the question of whether existing consent instruments are sufficient, logistically feasible, or even necessary, for data mining. In this chapter, we review the data collection and mining landscape, including commercial and academic activities, and the relevant data protection concerns, to determine the types of consent instruments used. Using three case studies, we use the new paradigm of human-data interaction to examine whether these existing approaches are appropriate. We then introduce an approach to consent that has been empirically demonstrated to improve on the state of the art and deliver meaningful consent. Finally, we propose some best practices for data collectors to ensure their data mining activities do not violate the expectations of the people to whom the data relate.

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Section: Introductionmentioning

confidence: 99%

Beyond the EULA: Improving Consent for Data Mining

Hutton

Henderson

2017

Studies in Big Data

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“…In a general sense, prior studies have suggested a potential privacy paradox: 'people want privacy, but do not want to pay for it, and in fact are willing to disclose sensitive information for even small rewards' (Acquisti, 2013). This is partly confirmed by a recent work of Staiano et al (2014) on the collection and sale of personally identifiable information, showing that people care about their privacy information but are also willing to sell contextual data. However, it remains unclear whether privacy matters in the case of donating data for scientific purposes.…”

Section: The Economics Of Revealing Informationmentioning

confidence: 69%

“…In this light, including a more specific purpose of data usage, such as developing better applications, may enable more concrete perceptions on the need of donation among participants. Prior studies show that people are willing to disclose sensitive information for even small rewards (Staiano et al, 2014). In our study, we did not provide any incentives to donors.…”

mentioning

confidence: 97%

Donating Context Data to Science: The Effects of Social Signals and Perceptions on Action-Taking

Yong¹,

Ferreira²,

Gonçalves³

et al. 2016

Interact. Comput.

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It is becoming increasingly easy for researchers to develop context-aware applications for smartphones. A perennial challenge, however, is to convince a large number of people to install them and donate contextual data for scientific purposes. Our empirical study seeks to address this challenge by investigating how people's perception and attitude affect their willingness to donate context data to researchers and quantifies the effects of social signals on donation action-taking. Our findings indicate that the perceived need for donation and perceived organization reputation are key determinants in deciding whether to donate: people with altruistic personality do not necessarily donate if they cannot see the need to take an action. Furthermore, we provide evidence that even if people indicate a willingness to donate, they are hesitant to take action towards donating data unless catalysts like social signals (hints about the actions of others) are present. RESEARCH HIGHLIGHTS • Social signal affects people's actual human-computer interaction data donation behavior. • Participants who saw social signal were five times more likely to become donors • Perceived organization reputation and need to donate are key determinants.

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“…Such context-awareness makes the self-configuration of privacy preferences a complex task [9], which may lead to a failure to protect location privacy [20]. Since people treat their location as the most valuable type of personal information [26], the lack of usable location-privacy protection mechanisms may affect the adoption of LSSs. To address these usability and information overload issues, recommenders have been designed to configure location-privacy preferences (semi-)automatically, for instance by recommending preferences based on crowdsourced results (e.g.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Privacy Concerns on Privacy Recommenders

Zhao

Henderson

2016

Proceedings of the 21st International Conference on Intelligent User Interfaces

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Location-sharing services such as Facebook and Foursquare/Swarm have become increasingly popular, due to the ease at which users can share their locations, and participate in services, games and other applications that leverage these locations. But it is important for people who use these services to configure appropriate location-privacy preferences so that they can control to whom they want to share their location information. Manually configuring these preferences may be burdensome and confusing, and so location-privacy preference recommenders based on crowdsourcing preferences from other users have been proposed. Whether people will accept the recommended preferences acquired from other users, who they may not know or trust, has not, however, been investigated.In this paper, we present a user experiment (n=99) to explore what factors influence people's acceptance of locationprivacy preference recommenders. We find that 44% of our participants have privacy concerns about such recommenders. These concerns are shown to have a negative effect (p <0.001) on their acceptance of the recommendations and their satisfaction about their choices. Furthermore, users' acceptance of recommenders varies according to both context and recommendations being made. Our findings are potentially useful to designers of location-sharing services and privacy recommenders.

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Money walks

Cited by 68 publications

References 48 publications

Beyond the EULA: Improving Consent for Data Mining

Beyond the EULA: Improving Consent for Data Mining

Donating Context Data to Science: The Effects of Social Signals and Perceptions on Action-Taking

The Effect of Privacy Concerns on Privacy Recommenders

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