Browsing by Subject "Online social networks"

Now showing 1 - 2 of 2

Open Access
Entering watch dogs*: evaluating privacy risks against large-scale facial search and data collection
(IEEE, 2021-07-19) Durmaz, Bahadır; Ayday, Erman
Discovering friends on online platforms have become relatively easier with the introduction of contact discovery and ability to search using phone numbers. Such features conveniently connect users by acting as unique tokens across platforms, as opposed to other attributes, such as user names. Using this feature, in this work, one of our contributions is to explore how an attacker can easily create a massive dataset of individuals residing in a given region (e.g., country) that includes high amount of personal information about such individuals. To identify the active social network accounts of individuals in a given region, we show that brute force phone number verification is possible in popular online services, such as WhatsApp, Facebook Messenger, and Twitter. We also go beyond and show the feasibility of collecting several data points on discovered accounts, including multiple facial data belonging to each account owner along with 23 other attributes. Then, as our main contribution, we quantify the privacy risk for an attacker linking a total stranger (e.g., someone it randomly comes across in public) to one of the collected records via facial features. Our results show that accurate facial search is possible in the constructed dataset and that an attacker can link a randomly taken photo (i.e., a single facial photo) of an individual to their profile with 67% accuracy. This means that an attacker can, on a large scale, create a search engine that is capable of identifying individuals' records efficiently and accurately from just a single facial photo.
Open Access
Towards modeling and mitigating misinformation propagation in online social networks
(2023-01) Yılmaz, Tolga
Misinformation on the internet and social media has become a pressing concern due to its potential impacts on society, undermining trust and impacting human decisions on global issues such as health, energy, politics, terrorism, and disasters. As a solution to the problem, computational methods have been employed to detect and mitigate the spread of false or misleading information. These efforts have included the development of algorithms to identify fake news and troll accounts, as well as research on the dissemination of misinformation on social media platforms. However, the problem of misinformation on the web and social networks remains a complex and ongoing challenge, requiring continued attention and research. We contribute to three different solution aspects of the problem. First, we design and implement an extensible social network simulation framework called Crowd that helps model, simulate, visualize and analyze social network scenarios. Second, we gamify misinformation propagation as a cooperative game between nodes and identify how misinformation spreads under various criteria. Then, we design a network-level game where the nodes are controlled from a higher perspective. In this game, we train and test a deep reinforcement learning method based on Multi-Agent Deep Deterministic Policy Gradients and show that our method outperforms well-known node-selection algorithms, such as page-rank, centrality, and CELF, over various social networks in defending against misinformation or participating in it. Finally, we promote and propose a blockchain and deep learning hybrid approach that utilizes crowdsourcing to target the misinformation problem while providing transparency, immutability, and validity of votes. We provide the results of extensive simulations under various combinations of well-known attacks on reputation systems and a case study that compares our results with a current study on Twitter.