Analyzing and Predicting Privacy Settings in the Social Web

Naini, Kaweh Djafari; Altıngövde, İsmail Sengör; Kawase, Ricardo; Herder, Eelco; Niederée, Claudia

doi:10.1007/978-3-319-20267-9_9

Cited by 12 publications

(3 citation statements)

References 23 publications

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“…They are far more likely to rely on default configurations than they are to fine-tune their cookie settings for each website [1,30]. In several cases, these default settings are privacy-invasive and favor the service providers, which results in privacy risks [33,37,38]. Several proposals have aimed at automating the interaction with cookie notices [16,34,42].…”

Section: Introductionmentioning

confidence: 99%

CookieEnforcer: Automated Cookie Notice Analysis and Enforcement

Khandelwal¹,

Nayak²,

Harkous³

et al. 2022

Preprint

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Online websites use cookie notices to elicit consent from the users, as required by recent privacy regulations like the GDPR and the CCPA. Prior work has shown that these notices use dark patterns to manipulate users into making website-friendly choices which put users' privacy at risk. In this work, we develop CookieEnforcer, a new system for automatically discovering cookie notices and deciding on the options that result in disabling all non-essential cookies. In order to achieve this, we first build an automatic cookie notice detector that utilizes the rendering pattern of the HTML elements to identify the cookie notices. Next, CookieEnforcer analyzes the cookie notices and predicts the set of actions required to disable all unnecessary cookies. This is done by modeling the problem as a sequence-tosequence task, where the input is a machine-readable cookie notice and the output is the set of clicks to make. We demonstrate the efficacy of CookieEnforcer via an end-to-end accuracy evaluation, showing that it can generate the required steps in 91% of the cases. Via a user study, we show that CookieEnforcer can significantly reduce the user effort. Finally, we use our system to perform several measurements on the top 5k websites from the Tranco list (as accessed from the US and the UK), drawing comparisons and observations at scale.

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Section: Introductionmentioning

confidence: 99%

CookieEnforcer: Automated Cookie Notice Analysis and Enforcement

Khandelwal¹,

Nayak²,

Harkous³

et al. 2022

Preprint

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“…To address usability issues in privacy protection, various machine learning classifiers have been used to predict privacy preferences in online social networks [8,22,7] and in LSSs [25,2], thereby helping people to configure their privacy rules (semi-)automatically. These methods learn from individual users' previous privacy decisions and make predictions based on the models that they build.…”

Section: Related Workmentioning

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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“…Automatic inference of privacy policies in content-sharing social networks has also been approached with machine learning techniques. The works in [ 39 , 40 , 41 ] apply classifiers to predict whether a post uploaded by a user has a low or a high privacy level. The work in [ 42 ] also uses machine learning to classify users into privacy setting classes based on a set of user features.…”

Section: Related Workmentioning

confidence: 99%

Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs

González-Tablas

Tapiador

2016

Sensors

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We address the problem of bootstrapping security and privacy policies for newly-deployed apps in wireless body area networks (WBAN) composed of smartphones, sensors and other wearable devices. We introduce a framework to model such a WBAN as an undirected graph whose vertices correspond to devices, apps and app resources, while edges model structural relationships among them. This graph is then augmented with attributes capturing the features of each entity together with user-defined tags. We then adapt available graph-based similarity metrics to find the closest app to a new one to be deployed, with the aim of reusing, and possibly adapting, its security policy. We illustrate our approach through a detailed smartphone ecosystem case study. Our results suggest that the scheme can provide users with a reasonably good policy that is consistent with the user’s security preferences implicitly captured by policies already in place.

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Analyzing and Predicting Privacy Settings in the Social Web

Cited by 12 publications

References 23 publications

CookieEnforcer: Automated Cookie Notice Analysis and Enforcement

CookieEnforcer: Automated Cookie Notice Analysis and Enforcement

The Effect of Privacy Concerns on Privacy Recommenders

Bootstrapping Security Policies for Wearable Apps Using Attributed Structural Graphs

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