State of Online Privacy: A Technical Perspective

Kelbert, Florian; Shirazi, Fatemeh; Simo, Hervais; Wüchner, Tobias; Buchmann, Johannes; Pretschner, Alexander; Waidner, Michael

doi:10.1007/978-3-642-31943-3_4

Cited by 6 publications

(8 citation statements)

References 127 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since we are reporting qualitative research results, like Wash [32], we will not report how many users alluded to each sub-theme. We do attempt to give a flavour by using terms like 'very few' (1-2 participants), 'some' (3)(4)(5)(6)(7)(8)(9)(10), 'many' (11)(12)(13)(14)(15)(16)(17)(18), and 'most' (19 or 20). We substantiate the various themes and sub-themes with (translated) quotes where applicable.…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…Long-range location information that is not privacy critical:Some participants believe that IP addresses only provides information of the country, region or city 'similar to the ZIP code' (15); 'country and region' (13). As such it is not considered as privacy critical information but information used to improve the service (like in the weather forecast example).…”

Section: Ip Addressmentioning

confidence: 97%

“…And I think there is much more behind it, then the spy from BND' (8). Some mentioned that it does not matter how much effort they spend 'they will know everything about me' (19); and that also in general '(not mentioning governmental institutions) it is difficult to protect against advanced attacks without expending 'enormous effort' (6) or 'stop using the Internet' (13).…”

Section: People Are Not Aware Of Countermeasuresmentioning

confidence: 99%

“…In particular, how users are identified and how their online activities are (implicetly) tracked. Tracking technologies and countermeasures against them have been extensively reviewed in the literature [17,13,8]. In this section we review commonly used identifying and tracking techniques (possible consequences for tracked people) and their countermeasures.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

What Deters Jane from Preventing Identification and Tracking on the Web?

Shirazi

Volkamer

2014

Proceedings of the 13th Workshop on Privacy in the Electronic Society

Self Cite

View full text Add to dashboard Cite

Recent revelations about surveillance by several institutions and de-identification can be expected to have increased public awareness of identification-and tracking-related privacy threats. It is reasonable to expect that the general public has started using corresponding privacy protection mechanisms. Our goal with this research was to determine whether they actually do this. If not, we wanted to explore possible explanations for not uptaken such privacy-protecting countermeasures. We interviewed 20 (mainly lay) people and found that our interviewees did not proactively try to prevent being identified and tracked. We identified seven different types of explanations. Including a number of misconceptions which might explain this puzzling level of apathy. The participants demonstrated confusion between different kinds of sensitive data; and displayed a confusion between the semantics of 'privacy' and 'security'. The findings also indicate that security being compromised, resulting in losing money for example, is more concrete and more easily brought to mind than privacy-related problems.

show abstract

Section: Evaluation Methodologymentioning

confidence: 99%

Section: Ip Addressmentioning

confidence: 97%

Section: People Are Not Aware Of Countermeasuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

What Deters Jane from Preventing Identification and Tracking on the Web?

Shirazi

Volkamer

2014

Proceedings of the 13th Workshop on Privacy in the Electronic Society

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yet, once a TPA retrieved user data, it remains unclear whether, how and where this data is stored, processed, and disseminated [3,8,15,20]. Noteworthy, TPAs that integrate into major OSNs are obliged to present a privacy policy and terms of service to users.…”

mentioning

confidence: 99%

Compliance Monitoring of Third-Party Applications in Online Social Networks

Kelbert

Fromm

2016

2016 IEEE Security and Privacy Workshops (SPW)

Self Cite

View full text Add to dashboard Cite

Abstract-With the widespread adoption of Online Social Networks (OSNs), users increasingly also use corresponding thirdparty applications (TPAs), such as social games and applications for collaboration. To improve their social experience, TPAs access users' personal data via an API provided by the OSN. Applications are then expected to comply with certain security and privacy policies when handling the users' data. However, in practice, they might store, use, and distribute that data in all kinds of unapproved ways. We present an approach that transparently enforces security and privacy policies on TPAs that integrate with OSNs. To this end, we integrate concepts and implementations from the research areas of data usage control and information flow control. We instantiate these results in the context of TPAs in OSNs in order to enforce compliance with security and privacy policies that are provided by the OSN operator. We perform a preliminary evaluation of our approach on the basis of a TPA that integrates with the Facebook API.

show abstract

AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases

Park

Chai

2021

Applied Sciences

View full text Add to dashboard Cite

Since there are growing concerns regarding online privacy, firms may have the risk of being involved in various privacy infringement cases resulting in legal causations. If firms are aware of consequences from possible cases of invasion of online privacy, they can more actively prevent future online privacy infringements. Thus, this study attempts to predict the probability of judgment types caused by various invasions within US judicial cases that are related to online privacy invasions. Since legal judgment results are significantly influenced by societal factors and technological development, this study tries to identify a model that can accurately predict legal judgment with explainability. To archive the study objective, it compares the prediction performance by applying five types of classification algorithms (LDA, NNET, CART, SVM, and random forest) of machine learning. We also examined the relationship between privacy infringement factors and adjudications by applying network text analysis. The results indicate that firms could have a high possibility of both civil and criminal law responsibilities if they distributed malware or spyware, intentionally or non-intentionally, to collect unauthorized data. It addresses the needs of reflecting both quantitative and qualitative approach for establishing automatic legal systems for improving its accuracy based on the socio-technical perspective.

show abstract

State of Online Privacy: A Technical Perspective

Cited by 6 publications

References 127 publications

What Deters Jane from Preventing Identification and Tracking on the Web?

What Deters Jane from Preventing Identification and Tracking on the Web?

Compliance Monitoring of Third-Party Applications in Online Social Networks

AI Model for Predicting Legal Judgments to Improve Accuracy and Explainability of Online Privacy Invasion Cases

Contact Info

Product

Resources

About