Collective Exposure: Peer Effects in Voluntary Disclosure of Personal Data

Böhme, Rainer; Pötzsch, Stefanie

doi:10.1007/978-3-642-27576-0_1

Cited by 10 publications

(7 citation statements)

References 17 publications

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“…However, this study is inconclusive about the overall decision efficiency because the decision effort, measured in time to completion, increased with the strong cue, and the quality of the cue was not endogenized in this setup. The very fact that users follow other users when uncertain about privacy-relevant actions is also supported with empirical evidence from a larger field study in the context of online social lending [12].…”

mentioning

confidence: 57%

The security cost of cheap user interaction

Böhme

Großklags

2011

Proceedings of the 2011 New Security Paradigms Workshop

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Human attention is a scarce resource, and lack thereof can cause severe security breaches. As most security techniques rely on considerate human intervention in one way or another, this resource should be consumed economically. In this context, we postulate the view that every false alarm or unnecessary user interaction imposes a negative externality on all other potential consumers of this chunk of attention. The paper identifies incentive problems that stimulate overconsumption of human attention in security applications. It further outlines a lump-of-attention model, devised against the backdrop of established theories in the behavioral sciences, and discusses incentive mechanisms to fix the misallocation problem in security notification, for instance the idea of a Pigovian tax on attention consumption.

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mentioning

confidence: 57%

The security cost of cheap user interaction

Böhme

Großklags

2011

Proceedings of the 2011 New Security Paradigms Workshop

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“…They involve the three data-sharing criteria that model the involved trust (data collectors) and risks (data type and context), and they are the ones that explain malleable data-sharing behaviors [13,7,39]. This large spectrum comes in contrast to earlier experiments and field tests made within a context and involving a specific data-sharing scenario such as online social lending [40], crowdfunding [41] and commerce [16,13,42,43].…”

Section: Sensor Type Data Collector Contextmentioning

confidence: 99%

Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence

Pournaras¹,

Ballandies²,

Bennati³

et al. 2023

Preprint

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Collective privacy loss becomes a colossal problem, an emergency for personal freedoms and democracy. But, are we prepared to handle personal data as scarce resource and collectively share data under the doctrine: as little as possible, as much as necessary? We hypothesize a significant privacy recovery if a population of individuals, the data collective, coordinates to share minimum data for running online services with the required quality. Here we show how to automate and scale-up complex collective arrangements for privacy recovery using decentralized artificial intelligence. For this, we compare for first time attitudinal, intrinsic, rewarded and coordinated data sharing in a rigorous living-lab experiment of high realism involving >27,000 data-sharing choices. Using causal inference and cluster analysis, we differentiate criteria predicting privacy and five key data-sharing behaviors. Strikingly, data-sharing coordination proves to be a win-win for all: remarkable privacy recovery for people with evident costs reduction for service providers.

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“…Findings by Acquisti et al (2012) and Chang et al (2016) indicate that people are more willing to answer sensitive questions or disclose sensitive pictures, respectively, when knowing that others did so. On online mircrofinance platforms, Böhme and Pötzsch (2011) find that borrowers adapt their loan request to the most recent requests listed on the top of the starting page regarding how much to write, whether to add a photo, what personal information to disclose, and how identifiable to present oneself. Results regarding adaptations within the same loan category further suggest positive peer effects, but are less conclusive.…”

Section: Literaturementioning

confidence: 99%

“…Particularly, we provide novel evidence on the dynamics created by the interplay of strategic incentives and peer comparison. Acquisti et al (2012), Böhme and Pötzsch (2011), and Chang et al (2016) show that peer comparison spurs one's personal information disclosure behavior. We provide causal evidence that such peer effects may be especially pronounced under competition for benefits and affect also those who are initially unwilling to disclose information.…”

Section: Introductionmentioning

confidence: 99%

“…The mircrofinance platform Kickstarter even recommends borrowers to include soft, personal information into their requests. Second, one may react to the information disclosure behavior of one's peers, i.e., the more others share, the more likely one adapts their behavior (Acquisti et al 2012;Böhme and Pötzsch 2011;Chang et al 2016). This effect might be especially pronounced in competitive settings.…”

Section: Introductionmentioning

confidence: 99%

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Personal Information Disclosure under Competition for Benefits: Is Sharing Caring?

Ackfeld

Güth

2019

SSRN Journal

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Personal information is shared extensively every day, partly in exchange for benefits or as a reaction to other people's information sharing. In this paper, we experimentally investigate these two motives by analyzing the interaction of peer comparison and incentives to disclose potentially privacy-sensitive information. We find that information sharing is higher under incentives, and further increases under peer comparison. This effect is driven by those initially disclosing less, who additionally report to feel more compelled to reveal information. Our results provide an explanation for the current information sharing trend while pointing to a potentially neglected side effect.

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Collective Exposure: Peer Effects in Voluntary Disclosure of Personal Data

Cited by 10 publications

References 17 publications

The security cost of cheap user interaction

The security cost of cheap user interaction

Collective Privacy Recovery: Data-sharing Coordination via Decentralized Artificial Intelligence

Personal Information Disclosure under Competition for Benefits: Is Sharing Caring?

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