Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

Peddinti, Sai Teja; Korolova, Aleksandra; Bursztein, Elie; Sampemane, Geetanjali

doi:10.1109/sp.2014.38

Cited by 26 publications

(28 citation statements)

References 19 publications

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“…The privacy implications of our findings are even more alarming when considering the prominent role that platforms like Twitter play in protests and other forms of social activism [36]. Indeed, a substantial number of users choose to not reveal their actual identity on Twitter, and prior work has found a correlation between the choice of anonymity and the sensitivity of topics in tweets [54] 1 and other online posts [53].…”

Section: Introductionmentioning

confidence: 90%

Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data

Drakonakis¹,

Ilia

Ioannidis

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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The exposure of location data constitutes a significant privacy risk to users as it can lead to de-anonymization, the inference of sensitive information, and even physical threats. In this paper we present LPAuditor, a tool that conducts a comprehensive evaluation of the privacy loss caused by publicly available location metadata. First, we demonstrate how our system can pinpoint users' key locations at an unprecedented granularity by identifying their actual postal addresses. Our experimental evaluation on Twitter data highlights the effectiveness of our techniques which outperform prior approaches by 18.9%-91.6% for homes and 8.7%-21.8% for workplaces. Next we present a novel exploration of automated private information inference that uncovers "sensitive" locations that users have visited (pertaining to health, religion, and sex/nightlife). We find that location metadata can provide additional context to tweets and thus lead to the exposure of private information that might not match the users' intentions.We further explore the mismatch between user actions and information exposure and find that older versions of the official Twitter apps follow a privacy-invasive policy of including precise GPS coordinates in the metadata of tweets that users have geotagged at a coarse-grained level (e.g., city). The implications of this exposure are further exacerbated by our finding that users are considerably privacy-cautious in regards to exposing precise location data. When users can explicitly select what location data is published, there is a 94.6% reduction in tweets with GPS coordinates. As part of current efforts to give users more control over their data, LPAuditor can be adopted by major services and offered as an auditing tool that informs users about sensitive information they (indirectly) expose through location metadata.

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Section: Introductionmentioning

confidence: 90%

Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data

Drakonakis¹,

Ilia

Ioannidis

et al. 2019

Proceedings 2019 Network and Distributed System Security Symposium

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“…Our proposal is based on the existence of a mechanism that is able to determine the topics discussed by the pieces of information that are exchanged in OSNs (i.e., the messages). Topics here can be categories predefined by the underlying OSN infrastructure [54]; usergenerated tags like Flickr categories [53]; or categories or tags extracted from images [66,80], videos [3], geolocation information [45], or text [57,74,12].…”

Section: Topic Analysismentioning

confidence: 99%

Implicit Contextual Integrity in Online Social Networks

Criado¹,

Such²

2015

Information Sciences

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Many real incidents demonstrate that users of Online Social Networks need mechanisms that help them manage their interactions by increasing the awareness of the different contexts that coexist in Online Social Networks and preventing them from exchanging inappropriate information in those contexts or disseminating sensitive information from some contexts to others. Contextual integrity is a privacy theory that conceptualises the appropriateness of information sharing based on the contexts in which this information is to be shared. Computational models of Contextual Integrity assume the existence of well-defined contexts, in which individuals enact pre-defined roles and information sharing is governed by an explicit set of norms. However, contexts in Online Social Networks are known to be implicit, unknown a priori and ever changing; users relationships are constantly evolving; and the information sharing norms are implicit. This makes current Contextual Integrity models not suitable for Online Social Networks. In this paper, we propose the first computational model of Implicit Contextual Integrity, presenting an information model and an Information Assistant Agent that uses the information model to learn implicit contexts, relationships and the information sharing norms to help users avoid inappropriate information exchanges and undesired information disseminations. Through an experimental evaluation, we validate the properties of Information Assistant Agents, which are shown to: infer the information sharing norms even if a small proportion of the users follow the norms and in presence of malicious users; help reduce the exchange of inappropriate information and the dissemination of sensitive information with only a partial view of the system and the information received and sent by their users; and minimise the burden to the users in terms of raising unnecessary alerts.Comment: Authors Version of the paper accepted for publication in the Information Sciences journal (http://www.journals.elsevier.com/information-sciences/

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“…As pointed out in [36], there is no universal definition of what constitutes sensitive content. For this analysis, we create a second dataset by selecting several broad topic categories that are widely considered sensitive and/or controversial by many-pornography, escort services, sexual orientation, religious and racial hatred, online drugs, and guns.…”

Section: Followers Of Sensitive and Non-sensitive Accountsmentioning

confidence: 99%

“…A study about 4chan, an image board website, showed that online communities can succeed despite being fully anonymous and extremely ephemeral [5]. By analyzing a question-and-answer websiteQuora, Peddinti et al show that it is possible to gain a novel understanding of users' perspectives on content sensitivity via a data-driven analysis of their usage of anonymity features of the website [36].…”

Section: Data Driven Studiesmentioning

confidence: 99%

“…Although both on-line and off-line anonymity has been considered by researchers in psychology and sociology, as discussed in Section 7, these studies have generally been carried out with small data sets and surveys. There have also been a few data-driven studies of anonymity in blogs and postings to Web sites [16,5,36]. To our knowledge, this paper is the first to conduct a large-scale data-driven analysis of user anonymity in online social networks.…”

Section: Introductionmentioning

confidence: 99%

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"On the internet, nobody knows you're a dog"

Peddinti

Ross

Cappos

2014

Proceedings of the Second ACM Conference on Online Social Networks

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Twitter does not impose a Real-Name policy for usernames, giving users the freedom to choose how they want to be identified. This results in some users being Identifiable (disclosing their full name) and some being Anonymous (disclosing neither their first nor last name).In this work we perform a large-scale analysis of Twitter to study the prevalence and behavior of Anonymous and Identifiable users. We employ Amazon Mechanical Turk (AMT) to classify Twitter users as Highly Identifiable, Identifiable, Partially Anonymous, and Anonymous. We find that a significant fraction of accounts are Anonymous or Partially Anonymous, demonstrating the importance of Anonymity in Twitter. We then select several broad topic categories that are widely considered sensitive-including pornography, escort services, sexual orientation, religious and racial hatred, online drugs, and guns-and find that there is a correlation between content sensitivity and a user's choice to be anonymous. Finally, we find that Anonymous users are generally less inhibited to be active participants, as they tweet more, lurk less, follow more accounts, and are more willing to expose their activity to the general public. To our knowledge, this is the first paper to conduct a large-scale data-driven analysis of user anonymity in online social networks.

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Cloak and Swagger: Understanding Data Sensitivity through the Lens of User Anonymity

Cited by 26 publications

References 19 publications

Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data

Please Forget Where I Was Last Summer: The Privacy Risks of Public Location (Meta)Data

Implicit Contextual Integrity in Online Social Networks

"On the internet, nobody knows you're a dog"

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