The website fingerprinting (or inter-domain WSF), enhanced by various machine learning techniques, has shown its power to identify websites a user has visited. To our best knowledge, a finer-grained problem of web page fingerprinting (or intra-domain WPF) has not been systematically studied by our research community. The WPF attackers, such as government agencies enforcing Internet censorship, are keen to identify the particular web pages (e.g., a political dissident's social media page) visited by the target user.In this work, we investigate the intra-domain WPF among social media websites, against the realistic on-path passive attack scenario. We reveal that delivering large-size data such as images and videos via Content Delivery Networks (CDNs), which is a common practice in social media websites, makes intra-domain WPF highly feasible. The network traffic generated during rendering a social media page exhibits temporal and volumetric patterns that are sufficiently recognizable by machine learning algorithms. We characterize such patterns as CDN bursts, and use features extracted from them to empower classification algorithms to achieve a high classification accuracy (96%) and a low false positive rate (0.02%).
Coupling learning is designed to estimate, discover and extract the interactions and relationships among learning components. It provides insights into complex interactive data, and has been extensively incorporated into recommender systems to enhance the interpretability of sophisticated relationships between users and items. Coupling learning can be further fostered once the trending collaborative learning can be engaged to take advantage of the cross-platform data. To facilitate this, privacy-preserving solutions are in high demand-it is desired that the collaboration should not expose either the private data of each individual owner or the model parameters trained on their datasets. In this work, we develop a distributed collaborative coupling learning system which enables differential privacy. The proposed system defends against the adversary who has gained full knowledge of the training mechanism and the access to the model trained collaboratively. It also addresses the privacy-utility tradeoff by a provable tight sensitivity bound. Our experiments demonstrate that the proposed system guarantees favourable privacy gains at a modest cost in recommendation quality, even in scenarios with a large number of training epochs. Index Terms-Coupling learning, differential privacy, collaborative learning COUPLING LEARNING is an emerging research topic that refers to understanding, formalizing and quantifying the complex relations and interactions, i.e., couplings hidden in complex data. Effective discovery and extraction of the
This letter proposes a hybrid approach combining Self-Adaptive Mathematical Morphology (SAMM) and Time-Frequency (TF) techniques to authenticate the source information of Distribution Synchrophasors (DS) within nearrange locations. The SAMM can adaptively regulate the synchrophasors variations which are representatives of local environmental characteristics. Subsequently, TF mapping is employed to extract informative signatures from the regulated synchrophasors variation. Finally, Random Forest Classification (RFC) is used to correlate the extracted signatures with the source information based on the derived TF mapping. Experiment results using DS collected at multiple small geographical scales validated the proposed methodology.
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