- Watermarking and Steganography
- Signal Processing and Cryptography
- Multimedia Forensics
- Communications and Network Security
- Biometrics
- Applications
In this paper, we develop a framework to achieve a desirable trade-off between fairness, inference accuracy and privacy protection in the inference as service scenario. Instead of sending raw data to the cloud, we conduct a random mapping of the data, which will increase privacy protection and mitigate bias but reduce inference accuracy. To properly address the trade-off, we formulate an optimization problem to find the optimal transformation map. As the problem is nonconvex in general, we develop an iterative algorithm to find the desired map.
poster_V1.pdf
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- Read more about Slides for SCALABLE PRIVACY-PRESERVING DISTRIBUTED EXTREMELY RANDOMIZED TREES FOR STRUCTURED DATA WITH MULTIPLE COLLUDING PARTIES
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Today, in many real-world applications of machine learning algorithms, the data is stored on multiple sources instead of at one central repository. In many such scenarios, due to privacy concerns and legal obligations, e.g., for medical data, and communication/computation overhead, for instance for large-scale data, the raw data cannot be transferred to a center for analysis. Therefore, new machine learning approaches are proposed for learning from the distributed data in such settings. In this paper, we extend the distributed Extremely Randomized Trees (ERT) approach w.r.t.
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- Read more about Looking through Walls: Inferring Scenes from Video-Surveillance Encrypted Traffic
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Nowadays living environments are characterized by networks of inter-connected sensing devices that accomplish different tasks, e.g., video-surveillance of an environment by a network of CCTV cameras. A malicious user could gather sensitive details on people’s activities by eavesdropping the exchanged data packets. To overcome this problem,video streams are protected by encryption systems, but even secured channels may still leak some information.
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- Read more about SEMI-SUPERVISED FEATURE EMBEDDING FOR DATA SANITIZATION IN REAL-WORLD EVENTS
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With the rapid growth of data sharing through social media networks, determining relevant data items concerning a particular subject becomes paramount. We address the issue of establishing which images represent an event of interest through a semi-supervised learning technique. The method learns consistent and shared features related to an event (from a small set of examples) to propagate them to an unlabeled set. We investigate the behavior of five image feature representations considering low- and high-level features and their combinations.
slides_icassp2021.pdf
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- Read more about Secure Identification for Gaussian Channels
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New applications in modern communications are demanding robust and ultra-reliable low latency information exchange such as machine-to-machine and human-to-machine communications. For many of these applications, the identification approach of Ahlswede and Dueck is much more efficient than the classical transmission scheme proposed by Shannon. Previous studies concentrate mainly on identification over discrete channels. We focus on Gaussian channels for their known practical relevance. We deal with secure identification over Gaussian channels.
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- Read more about Secure Identification for Gaussian Channels
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New applications in modern communications are demanding robust and ultra-reliable low latency information exchange such as machine-to-machine and human-to-machine communications. For many of these applications, the identification approach of Ahlswede and Dueck is much more efficient than the classical transmission scheme proposed by Shannon. Previous studies concentrate mainly on identification over discrete channels. We focus on Gaussian channels for their known practical relevance. We deal with secure identification over Gaussian channels.
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- Read more about Efficient Capon-based approach exploiting temporal windowing for electric network frequency estimation
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Electric Network Frequency (ENF) fluctuations constitute a powerful tool in multimedia forensics. An efficient approach for ENF estimation is introduced with temporal windowing based on the filter-bank Capon spectral estimator. A type of Gohberg-Semencul factorization of the model covariance matrix is used due to the Toeplitz structure of the covariance matrix. Moreover, this approach uses, for the first time in the field of ENF, a temporal window, not necessarily the rectangular one, at the stage preceding spectral estimation.
poster2019.pdf
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- Read more about FuseLoc: A CCA Based Information Fusion for Indoor Localization Using CSI Phase and Amplitude of WiFi Signals
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With the growth of location based services, indoor localization is attracting great interests as it facilitates further ubiquitous environments. In this paper, we propose FuseLoc, the first information fusion based indoor localization using multiple features extracted from Channel State Information (CSI). In FuseLoc, the localization problem is modelled as a pattern matching problem, where the location of a subject is predicted based on the similarity measure of the CSI features of the unknown location with those of the training locations.
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