Monitoring and recommending privacy settings in social networks

Ghazinour, Kambiz; Matwin, Stan; Sokolova, Marina

doi:10.1145/2457317.2457344

Cited by 38 publications

(21 citation statements)

References 7 publications

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“…Researchers also provided public awareness of privacy risks associated with images shared online [Henne et al 2013;Xu et al 2015]. Along this line, several works are carried out to study users' privacy concerns in social networks, privacy decisions about sharing resources, and the risk associated with them [Ghazinour et al 2013;Gross and Acquisti 2005;Ilia et al 2015;Krishnamurthy and Wills 2008;Parra-Arnau et al 2014;Parra-Arnau et al 2012;Simpson 2008].…”

Section: Related Workmentioning

confidence: 99%

Image Privacy Prediction Using Deep Neural Networks

Tonge

Caragea

2020

ACM Trans. Web

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Images today are increasingly shared online on social networking sites such as Facebook, Flickr, Foursquare, and Instagram. Image sharing occurs not only within a group of friends but also more and more outside a user's social circles for purposes of social discovery. Despite that current social networking sites allow users to change their privacy preferences, this is often a cumbersome task for the vast majority of users on the Web, who face difficulties in assigning and managing privacy settings. When these privacy settings are used inappropriately, online image sharing can potentially lead to unwanted disclosures and privacy violations. Thus, automatically predicting images' privacy to warn users about private or sensitive content before uploading these images on social networking sites has become a necessity in our current interconnected world.In this paper, we explore learning models to automatically predict appropriate images' privacy as private or public using carefully identified image-specific features. We study deep visual semantic features that are derived from various layers of Convolutional Neural Networks (CNNs) as well as textual features such as user tags and deep tags generated from deep CNNs. Particularly, we extract deep (visual and tag) features from four pre-trained CNN architectures for object recognition, i.e., AlexNet, GoogLeNet, VGG-16, and ResNet, and compare their performance for image privacy prediction. Among all four networks, we observe that ResNet produces the best feature representations for this task. We also fine-tune the pre-trained CNN architectures on our privacy dataset and compare their performance with the models trained on pre-trained features. The results show that even though the overall performance obtained using the fine-tuned networks is comparable to that of pre-trained networks, the fine-tuned networks provide an improved performance for the private class as compared to models trained on the pre-trained features. Results of our experiments on a Flickr dataset of over thirty thousand images show that the learning models trained on features extracted from ResNet outperform the state-of-the-art models for image privacy prediction. We further investigate the combination of user tags and deep tags derived from CNN architectures using two settings: (1) SVM on the bag-of-tags features; and (2) text-based CNN. We compare these models with the models trained on ResNet visual features obtained for privacy prediction. Our results show that even though the models trained

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Section: Related Workmentioning

confidence: 99%

Image Privacy Prediction Using Deep Neural Networks

Tonge

Caragea

2020

ACM Trans. Web

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“…Online Image Privacy. Several works are carried out to study users' privacy concerns in social networks, privacy decisions about sharing resources, and the risk associated with them [15,19,24,36,37,42]. Ahern et al [1] examined privacy decisions and considerations in mobile and online photo sharing.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Deep Multi-modal Fusion for Image Privacy Prediction

Tonge

Caragea

2019

The World Wide Web Conference

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With millions of images that are shared online on social networking sites, e ective methods for image privacy prediction are highly needed. In this paper, we propose an approach for fusing object, scene context, and image tags modalities derived from convolutional neural networks for accurately predicting the privacy of images shared online. Speci cally, our approach identi es the set of most competent modalities on the y, according to each new target image whose privacy has to be predicted. e approach considers three stages to predict the privacy of a target image, wherein we rst identify the neighborhood images that are visually similar and/or have similar sensitive content as the target image. en, we estimate the competence of the modalities based on the neighborhood images. Finally, we fuse the decisions of the most competent modalities and predict the privacy label for the target image. Experimental results show that our approach predicts the sensitive (or private) content more accurately than the models trained on individual modalities (object, scene, and tags) and prior privacy prediction works. Also, our approach outperforms strong baselines, that train meta-classi ers to obtain an optimal combination of modalities. CCS CONCEPTS•Security and privacy → Social network security and privacy; KEYWORDS Image privacy prediction; fusion of modalities; decision-level fusion ACM Reference format:

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“…Although such interfaces can be efficient for the setting task alone, it is hard to have a clear overview on the current state of the settings as a whole, and which settings might be unusual and need some tuning [1]. Furthermore, it is obvious that they are not perceived as attractive and fun to use: Research in the past has shown that tuning the privacy settings is mostly perceived as a burdensome and boring task [3,8], which leads users to almost never adjust the standard settings, resulting in suboptimal privacy settings.…”

Section: Related Workmentioning

confidence: 99%

URetail: Privacy User Interfaces for Intelligent Retail Stores

Raber

Vossebein

2017

Human-Computer Interaction – INTERACT 2017

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Amazon recently opened its first intelligent retail store, which captures shopper movements, picked-up products and much more sensitive data. In this paper we present a privacy UI, called URetail, that returns to the customer control over his own data, by offering an interface to select which of his private data items should be disclosed. We use a radar metaphor to arrange the permissions with ascending sensitivity into different clusters, and introduce a new multi-dimensional form of a radar interface called the privacy pyramid. We conducted an expert interview and a pilot study to determine which types of data are recorded in an intelligent retail store, and grouped them with ascending sensitivity into clusters. A preliminary evaluation study shows that radar interfaces have their own strengths and weaknesses compared to a conventional UI.

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Monitoring and recommending privacy settings in social networks

Cited by 38 publications

References 7 publications

Image Privacy Prediction Using Deep Neural Networks

Image Privacy Prediction Using Deep Neural Networks

Dynamic Deep Multi-modal Fusion for Image Privacy Prediction

URetail: Privacy User Interfaces for Intelligent Retail Stores

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