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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 1/2017

16.11.2016 | Regular Paper

Shall I post this now? Optimized, delay-based privacy protection in social networks

verfasst von: Javier Parra-Arnau, Félix Gómez Mármol, David Rebollo-Monedero, Jordi Forné

Erschienen in: Knowledge and Information Systems | Ausgabe 1/2017

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Abstract

Despite the several advantages commonly attributed to social networks such as easiness and immediacy to communicate with acquaintances and friends, significant privacy threats provoked by unexperienced or even irresponsible users recklessly publishing sensitive material are also noticeable. Yet, a different, but equally significant privacy risk might arise from social networks profiling the online activity of their users based on the timestamp of the interactions between the former and the latter. In order to thwart this last type of commonly neglected attacks, this paper proposes an optimized deferral mechanism for messages in online social networks. Such solution suggests intelligently delaying certain messages posted by end users in social networks in a way that the observed online activity profile generated by the attacker does not reveal any time-based sensitive information, while preserving the usability of the system. Experimental results as well as a proposed architecture implementing this approach demonstrate the suitability and feasibility of our mechanism.
Vorheriger Artikel Synchronization-based scalable subspace clustering of high-dimensional data
Nächster Artikel Identification of critical situations via Event Processing and Event Trust Analysis

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Fußnoten
1
Aggregated Facebook and Twitter activity profiles are shown in [7] per country, during and before Ramadan.
 
2
SocialNumber (http://​www.​socialnumber.​com) is an example of such networks, where users must choose a unique number as identifier.
 
3
Clearly, if the attacker was the social networking platform, any information disclosed by the user would be known to the adversary.
 
4
According to [35], the data minimization principle means that a data controller, e.g., the social networking platform, should restrict the collection of personal data to what is strictly necessary to achieve its purpose. Also, it implies that the controller should store the data only for as long as is necessary to fulfill the purpose for which the information was collected.
 
5
A tweet is a message sent using Twitter.
 
6
This is in contrast to other information systems where user data (e.g., tags, queries or ratings) are simpler to process.
 
7
Messages exchanged on Twitter are publicly visible by default.
 
8
Those time instants are, in fact, time periods of 1 h each. In particular, the time index i consists in the interval \((i-1,i]\).
 
9
This would be, in fact, transparent to the user. The software installed on the user’s machine would decide whether a message is to be delayed or not.
 
10
Here users’ profiles do not capture their interests, but their online activity.
 
11
Recall from Sect. 3.5 that Shannon’s entropy is regarded here as a measure of privacy gain, whereas the KL divergence is interpreted as a measure of privacy risk.
 
13
We omit the distribution of the critical deferral rate for the uniform strategy since, as commented in Sect. 6.2, this strategy achieves critical privacy only when \(\varphi =1\). Consequently, the PMF of the critical rate is the trivial Dirac delta function centered at 1.
 
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Metadaten
Titel
Shall I post this now? Optimized, delay-based privacy protection in social networks
verfasst von
Javier Parra-Arnau
Félix Gómez Mármol
David Rebollo-Monedero
Jordi Forné
Publikationsdatum
16.11.2016
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 1/2017
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-016-1010-4

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