Automatic Detection of Sensitive Information in Educative Social Networks

Botti-Cebriá, Víctor; Val, Elena del; García-Fornes, Ana

doi:10.1007/978-3-030-57805-3_18

Cited by 11 publications

(11 citation statements)

References 19 publications

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“…Since its introduction by Nobel Prize winners Richard Thaler and Cass Sunstein, the "nudge" theory has been extensively investigated and applied repeatedly to the design of privacy-enhancing technologies. When it comes to OSNs, there is a wide variety of privacy nudges in the current literature whose goal is to support users' online self-disclosure decisions [7,9,10,16,30,38]. For instance, Raber et al [38] introduced PrivacyWedges, a visualization strategy for aiding the audience selection of social media publications.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Other nudging solutions precisely seek to warn users about publications containing sensitive information. Such is the case of Botti-Cebriá et al [9], who applied a compound of Natural Language Processing (NLP) techniques for determining whether a post contains information about one's location, health, and personal identifiers, among others. In line with this, Bracamonte et al [10] empirically assessed users' perception of warnings about the presence of personal information in their posts.…”

Section: Background and Related Workmentioning

confidence: 99%

“…This issue could be addressed by integrating a machine learning solution capable of classifying the content being disclosed by the user (i.e., picture, caption, or both). There is prior and ongoing research in this realm that could be leveraged for this purpose (e.g., [9,18,45]) and even commercial platforms offering services for the automatic classification of multimedia content (e.g., Microsoft Azure Computer Vision 2 and Google Vision AI 3 ). Hence, content-aware interventions could be (in principle) shaped by integrating such off-the-shelve solutions into ENAGRAM's architecture.…”

Section: Enagram's Benefits and Drawbacksmentioning

confidence: 99%

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ENAGRAM: An App to Evaluate Preventative Nudges for Instagram

Ferreyra,

Ostendorf,

Aïmeur

et al. 2022

Preprint

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Online self-disclosure is perhaps one of the last decade's most studied communication processes, thanks to the introduction of Online Social Networks (OSNs) like Facebook. Self-disclosure research has contributed significantly to the design of preventative nudges seeking to support and guide users when revealing private information in OSNs. Still, assessing the effectiveness of these solutions is often challenging since changing or modifying the choice architecture of OSN platforms is practically unfeasible. In turn, the effectiveness of numerous nudging designs is supported primarily by self-reported data instead of actual behavioral information. Objective: This work presents ENAGRAM, an app for evaluating preventative nudges, and reports the first results of an empirical study conducted with it. Such a study aims to showcase how the app (and the data collected with it) can be leveraged to assess the effectiveness of a particular nudging approach. Method: We used ENAGRAM as a vehicle to test a risk-based strategy for nudging the self-disclosure decisions of Instagram users. For this, we created two variations of the same nudge (i.e., with and without risk information) and tested it in a between-subjects experimental setting. Study participants (N=22) were recruited via Prolific and asked to use the app regularly for 7 days. An online survey was distributed at the end of the experiment to measure some privacy-related constructs. Results: From the data collected with ENAGRAM, we observed lower (though non-significant) self-disclosure levels when applying risk-based interventions. The constructs measured with the survey were not significant either, except for participants' External Information Privacy Concerns (EIPC). Implications: Our results suggest that (i) ENAGRAM is a suitable alternative for conducting longitudinal experiments in a privacy-friendly way, and (ii) it provides a flexible framework for the evaluation of a broad spectrum of nudging solutions.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

Section: Enagram's Benefits and Drawbacksmentioning

confidence: 99%

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ENAGRAM: An App to Evaluate Preventative Nudges for Instagram

Ferreyra,

Ostendorf,

Aïmeur

et al. 2022

Preprint

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“…Kaur et al [10] had made an attempt to survey various techniques for the prevention of data leakage, aside from certain challenges and data protection approaches, and also accounted for the constraints. Academics and professionals, we're able to draw benefits from the survey.Botti-Cebriá et al, [11]provided the sensitivity of the data that the user would share when published online. To this end, an assistant agent was proposed for the identification of sensitive data that was dependent on the message's distinct types of detected categories (that is, emotions, personal attacks, health, personal data, location, and so on).…”

Section: Related Workmentioning

confidence: 99%

Data Leak Identification in Social Networks Using Hybrid Tranquility K Means Clustering

Karthik¹,

Tamizhazhagan²,

Narayana³

2021

INDJCSE

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Loss of sensitive data can be stopped employing Data Leak Prevention (DLP). Most of such tools can be quite effective while protecting private information known already. At the same time, plenty of private information has not been recognized until it has been disclosed to various unknown users or other competition enterprises. Clustering refers to a data mining technique that can classify a certain set of instances into different clustersusing a measure of similarity. One of the most common algorithms based on partitioning is the K-Means. However, it has many drawbacks like it can generate local optimal solutions that are based on initial centroids that are chosen randomly. The Tabu Search (TS) Tranquility Search and the Stochastic Diffusion Search (SDS) have been proposed in this work. In one of the most recent algorithms called Tranquility Search, optimal global solutions are obtained by exploring through the entire solution space. Some studies show hybrid algorithms that are a combination of two different ideas producing better solutions. In this work, a new approach is presented which is a combination of two different ideas producing better solutions. The Improved Tranquility Search technique and the K-means algorithm are combined. For this, a hybrid Tranquility-TABU-SDS algorithm is applied in the social network for the DLP. The results of the experiment have proved that the method proposed performs better in comparison to other methods.

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“…In recent years, some proposals for assessing privacy on social networks have been made [1,4,29]. Most of these proposals involve the definition of metrics and mechanisms to improve trust and avoid the vulnerability of privacy in social networks [191,218].…”

Section: Pesedia: a Pedagogical Social Networkmentioning

confidence: 99%

A BDI Empathic Agent Model Based on a Multidimensional Cross-Cultural Emotion Representation

Aparicio¹

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Quisiera dar las gracias a mi familia por todo el cariño que me brinda, a mi pareja por soportarme durante todos estos años, tanto en los momentos buenos como en los no tan buenos, a mis amigos por animarme a seguir adelante, a los que caminaron a mi lado y ya no están, a mis directores Vicent y Emilio que, además de ser unos excelentes mentores, siempre me han apoyado ofreciéndome su consejo y mostrando una paciencia infinita incluso en los momentos de mayor debilidad y flaqueza. También quisiera agradecerles a mis compañeros de laboratorio y a mis compañeros del VRAIN por su apoyo y su colaboración y en especial a Bexy con la que compartí mis inicios en esta aventura.

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Automatic Detection of Sensitive Information in Educative Social Networks

Cited by 11 publications

References 19 publications

ENAGRAM: An App to Evaluate Preventative Nudges for Instagram

ENAGRAM: An App to Evaluate Preventative Nudges for Instagram

Data Leak Identification in Social Networks Using Hybrid Tranquility K Means Clustering

A BDI Empathic Agent Model Based on a Multidimensional Cross-Cultural Emotion Representation

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