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

Abstract

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.

On the Anonymity Risk of Time-Varying User Profiles

“…Precisely, the privacy-utility trade-off posed by the suppression of tags was investigated mathematically in [13], measuring privacy as the Shannon entropy of the perturbed profile, and utility as the percentage of tags users are willing to eliminate. Closely related to this are also other studies regarding the impact of suppressive PETs [15][16][17], where the impact of tag suppression is assessed experimentally in the context of various applications and real-world scenarios. This is particularly relevant when online services provide the users with the perception that sharing less data impacts their optimal service experience.…”

Identifying Experts in Question \& Answer Portals: A Case Study on Data Science Competencies in Reddit

“…Finally, by examining the prevalent traits in the out of scope group of comments, we can identify potentially malicious and unreliable users or social bots in the early stage and reduce their influence. As stated by Parra-Arnau et al, even though social networks provide an easy and immediate way of communication, there exist significant privacy threats provoked by inexperienced or even irresponsible users recklessly publishing sensitive material [Parra-Arnau et al, 2017]. For example, Pastor-Galindo et al [Pastor-Galindo et al, 2020] analysed the presence and behaviour of social bots in Twitter in the context of the Spanish general election.…”

Analysis, modelling and protection of online private data

“…Precisely, the the privacy-utility tradeoff posed by the suppression of tags was investigated mathematically [100,101,112], measuring privacy as the Shannon entropy of the perturbed profile, and utility as the percentage of tags users are willing to eliminate. Closely related to this are also other studies regarding the impact of suppressive PETs [98,103,112], where the impact of tag suppression is assessed experimentally in the context of various applications and real-world scenarios.…”