Are On-Line Personae Really Unlinkable?

Veeningen, Meilof; Piepoli, Antonio; Zannone, Nicola

doi:10.1007/978-3-642-54568-9_24

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…This simple example shows that only removing identifiable information from a dataset might not be sufficient to guarantee anonymization. Anonymized data can be re-identified by linking the data by means of other data sources [45,19]. Therefore, before disclosing a dataset containing highly sensitive information, data owners often transform it to reduce the risk that its records can be re-identified.…”

Section: Problem Statementmentioning

confidence: 99%

“…However, this in itself may not solve the problem, since removing unique identifiers might not be sufficient to guarantee data anonymity [49]. In fact, anonymized data could be de-anonymized through cross-referencing with data gathered from other sources [33,45]. Moreover, the application of machine learning techniques to anonymized data might still lead to the disclosure of sensitive and confidential information about data subjects, thanks to the power of current predictive models.…”

Section: Introductionmentioning

confidence: 99%

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A decision-support framework for data anonymization with application to machine learning processes

Caruccio

Desiato²,

Polese³

et al. 2022

Information Sciences

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Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A decision-support framework for data anonymization with application to machine learning processes

Caruccio

Desiato²,

Polese³

et al. 2022

Information Sciences

Self Cite

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“…(3) We introduce a set of metrics to express the relationship between the partial and the actual user profile. (4) We profile third-party http calls sent by Facebook tracking services and compare this to the the user actual profile. (5) We model user online footprints as a graph of the actions generated by each user and analyse the resulting graph structure, identifying known malicious trackers.…”

Section: Contributionmentioning

confidence: 99%

“…For example, to personalise their services or offer tailored advertising, web applications could use tracking services that identify a user through different networks [4] [5]. These tracking services usually combine information from different profiles that users create, for example their Gmail address or their Facebook or LinkedIn accounts.…”

Section: State Of the Artmentioning

confidence: 99%

On web user tracking of browsing patterns for personalised advertising

Puglisi

Rebollo-Monedero

Forné

2017

International Journal of Parallel, Emergent and Distributed Sys

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This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Parallel, Emergent and Distributed Systems on 19/02/2017, available online: http://www.tandfonline.com/doi/abs/10.1080/17445760.2017.1282480On today’s Web, users trade access to their private data for content and services. App and service providers want to know everything they can about their users, in order to improve their product experience. Also, advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users’ actions and tastes to suggest a range of products to buy. Both service providers and advertisers try to track users’ behaviour across their product network. For application providers this means tracking users’ actions within their platform. For third-party services following users, means being able to track them across different websites and applications. It is well known how, while surfing the Web, users leave traces regarding their identity in the form of activity patterns and unstructured data. These data constitute what is called the user’s online footprint. We analyse how advertising networks build and collect users footprints and how the suggested advertising reacts to changes in the user behaviour.Peer ReviewedPostprint (author's final draft

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“…For example, to personalise their services or offer tailored advertising, web applications could use tracking services that identify a user through different networks [2] [3]. These tracking services usually combine information from different profiles that users create, for example their Gmail address or their Facebook or LinkedIn accounts.…”

Section: Introductionmentioning

confidence: 99%

On Web user tracking: How third-party http requests track users' browsing patterns for personalised advertising

Puglisi

Rebollo-Monedero

Forné

2016

2016 Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)

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Abstract-On today's Web, users trade access to their private data for content and services. Advertising sustains the business model of many websites and applications. Efficient and successful advertising relies on predicting users' actions and tastes to suggest a range of products to buy. It follows that, while surfing the Web users leave traces regarding their identity in the form of activity patterns and unstructured data. We analyse how advertising networks build user footprints and how the suggested advertising reacts to changes in the user behaviour.

show abstract

Are On-Line Personae Really Unlinkable?

Cited by 8 publications

References 15 publications

A decision-support framework for data anonymization with application to machine learning processes

A decision-support framework for data anonymization with application to machine learning processes

On web user tracking of browsing patterns for personalised advertising

On Web user tracking: How third-party http requests track users' browsing patterns for personalised advertising

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