- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Chicago, IL, USA (2019.6.16-2019.6.21)] 2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) - Electron and hole partial specific resistances: a framework to understand contacts to solar cells
摘要： Several approaches have been proposed to anonymize relational databases using the criterion of k-anonymity, to avoid the disclosure of sensitive information by re-identification attacks. A relational database is said to meet the criterion of k-anonymity if each record is identical to at least (k ? 1) other records in terms of quasi-identifier attribute values. To anonymize a transactional database and satisfy the constraint of k-anonymity, each item must successively be considered as a quasi-identifier attribute. But this process greatly increases dimensionality, and thus also the computational complexity of anonymization, and information loss. In this paper, a novel efficient anonymization system called PTA is proposed to not only anonymize transactional data with a small information loss but also to reduce the computational complexity of the anonymization process. The PTA system consists of three modules, which are the Pre-processing module, the TSP module, and the Anonymity model, to anonymize transactional data and guarantees that at least k-anonymity is achieved: a pre-processing module, a traveling salesman problem module, and an anonymization module. Extensive experiments have been carried to compare the efficiency of the designed approach with the state-of-the-art anonymization algorithms in terms of scalability, runtime, and information loss. Results indicate that the proposed PTA system outperforms the compared algorithms in all respects.
关键词： Anonymity,privacy preserving data mining,TSP,divide-and-conquer,Gray sort
更新于2025-09-23 15:19:57
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[IEEE 2019 21st International Conference on Transparent Optical Networks (ICTON) - Angers, France (2019.7.9-2019.7.13)] 2019 21st International Conference on Transparent Optical Networks (ICTON) - Compact and Tunable Room Temperature THz Source from Quantum Dot Based Ultrafast Photoconductive Antennae
摘要： Several approaches have been proposed to anonymize relational databases using the criterion of k-anonymity, to avoid the disclosure of sensitive information by re-identification attacks. A relational database is said to meet the criterion of k-anonymity if each record is identical to at least (k ? 1) other records in terms of quasi-identifier attribute values. To anonymize a transactional database and satisfy the constraint of k-anonymity, each item must successively be considered as a quasi-identifier attribute. But this process greatly increases dimensionality, and thus also the computational complexity of anonymization, and information loss. In this paper, a novel efficient anonymization system called PTA is proposed to not only anonymize transactional data with a small information loss but also to reduce the computational complexity of the anonymization process. The PTA system consists of three modules, which are the Pre-processing module, the TSP module, and the Anonymity model, to anonymize transactional data and guarantees that at least k-anonymity is achieved: a pre-processing module, a traveling salesman problem module, and an anonymization module. Extensive experiments have been carried to compare the efficiency of the designed approach with the state-of-the-art anonymization algorithms in terms of scalability, runtime, and information loss. Results indicate that the proposed PTA system outperforms the compared algorithms in all respects.
关键词： Anonymity,privacy preserving data mining,TSP,divide-and-conquer,Gray sort
更新于2025-09-23 15:19:57
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Accurate and Scalable Image Clustering Based On Sparse Representation of Camera Fingerprint
摘要： Clustering images according to their acquisition devices is a well-known problem in multimedia forensics, which is typically faced by means of camera Sensor Pattern Noise (SPN). Such an issue is challenging since SPN is a noise-like signal, hard to be estimated and easy to be attenuated or destroyed by many factors. Moreover, the high dimensionality of SPN hinders large-scale applications. Existing approaches are typically based on the correlation among SPNs in the pixel domain, which might not be able to capture intrinsic data structure in union of vector subspaces. In this paper, we propose an accurate clustering framework, which exploits linear dependencies among SPNs in their intrinsic vector subspaces. Such dependencies are encoded under sparse representations which are obtained by solving a LASSO problem with non-negativity constraint. The proposed framework is highly accurate in number of clusters estimation and image association. Moreover, our framework is scalable to the number of images and robust against double JPEG compression as well as the presence of outliers, owning big potential for real-world applications. Experimental results on Dresden and Vision database show that our proposed framework can adapt well to both medium-scale and large-scale contexts, and outperforms state-of-the-art methods.
关键词： sparse subspace clustering,sensor pattern noise,divide-and-conquer,Image clustering
更新于2025-09-04 15:30:14