Manipulation and Malicious Personalization : Exploring the Self-Disclosure Biases Exploited by Deceptive Attackers on Social Media
| Autor: | Esma Aïmeur, Nicolás Emilio Díaz Ferreyra, Hicham Hage |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
media_common.quotation_subject
social media Internet privacy 02 engineering and technology Personalization deception Artificial Intelligence 020204 information systems malicious personalization 0202 electrical engineering electronic engineering information engineering cognitive biases Social media awareness Private information retrieval adaptive privacy media_common Original Research Multidisciplinary Social network business.industry Deception 16. Peace & justice Cognitive bias Informatik Incentive self-disclosure ComputingMilieux_COMPUTERSANDSOCIETY 020201 artificial intelligence & image processing business Psychology Personally identifiable information |
| Zdroj: | Frontiers in Artificial Intelligence |
| Popis: | In the real world, the disclosure of private information to others often occurs after a trustworthy relationship has been established. Conversely, users of Social Network Sites (SNSs) like Facebook or Instagram often disclose large amounts of personal information prematurely to individuals which are not necessarily trustworthy. Such a low privacy-preserving behavior is often exploited by deceptive attackers with harmful intentions. Basically, deceivers approach their victims in online communities using incentives that motivate them to share their private information, and ultimately, their credentials. Since motivations, such as financial or social gain vary from individual to individual, deceivers must wisely choose their incentive strategy to mislead the users. Consequently, attacks are crafted to each victim based on their particular information-sharing motivations. This work analyses, through an online survey, those motivations and cognitive biases which are frequently exploited by deceptive attackers in SNSs. We propose thereafter some countermeasures for each of these biases to provide personalized privacy protection against deceivers. CA extern |
| Databáze: | OpenAIRE |
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