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Titlebook: Data Privacy Management, Cryptocurrencies and Blockchain Technology; ESORICS 2020 Interna Joaquin Garcia-Alfaro,Guillermo Navarro-Arribas,J

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發(fā)表于 2025-3-21 17:04:54 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Privacy Management, Cryptocurrencies and Blockchain Technology
副標(biāo)題ESORICS 2020 Interna
編輯Joaquin Garcia-Alfaro,Guillermo Navarro-Arribas,Jo
視頻videohttp://file.papertrans.cn/264/263002/263002.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Data Privacy Management, Cryptocurrencies and Blockchain Technology; ESORICS 2020 Interna Joaquin Garcia-Alfaro,Guillermo Navarro-Arribas,J
描述This book constitutes the revised selected post conference proceedings of the 15th International Workshop on Data Privacy Management, DPM 2020, and the 4.th. International Workshop on Cryptocurrencies and Blockchain Technology, CBT 2020, held in conjunction with the 25th European Symposium on Research in Computer Security, ESORICS 2020, held in Guildford, UK in September 2020..For the CBT Workshop 8 full and 4 short papers were accepted out of 24 submissions. The selected papers are organized in the following topical headings: Transactions, Mining, Second Layer..and Inter-bank Payments. The DPM Workshop received 38 submissions from which 12 full and 5 short papers were selected for presentation. The papers focus on Second Layer, Signature Schemes, Formal Methods, Privacy, SNARKs and Anonymity..
出版日期Conference proceedings 2020
關(guān)鍵詞access control; authentication; communication systems; computer networks; computer science; computer secu
版次1
doihttps://doi.org/10.1007/978-3-030-66172-4
isbn_softcover978-3-030-66171-7
isbn_ebook978-3-030-66172-4Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Joaquin Garcia-Alfaro,Guillermo Navarro-Arribas,Jo
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發(fā)表于 2025-3-22 00:29:41 | 只看該作者
Fairness-Aware Privacy-Preserving Record Linkageer distorts from producing correct predictions with equal probabilities for individuals across different protected groups based on sensitive features (e.g. gender or race). Developing classifiers that are fair with respect to such sensitive features is an important problem for classification in gene
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Differentially Private Profiling of Anonymized Customer Purchase Recordse anonymizations, including top/bottom coding, sampling/suppression, and generalization (the fundamental techniques in .-anonymity). These anonymization are modeled by means of simple factors, which allow us to estimate the privacy loss and the mean absolute error under the assumption that the profi
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P-Signature-Based Blocking to Improve the Scalability of Privacy-Preserving Record Linkageking methods proposed to reduce the number of comparisons, they fall short in providing an efficient and effective solution for linking multiple large databases. Further, all private blocking methods are largely dependent on data. In this paper, we propose a novel private blocking method addressing
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發(fā)表于 2025-3-22 13:22:34 | 只看該作者
Utility Promises of , in Privacy Preserving Data Mining protection remains a challenge. In addition to this, existing approaches do not work well with high-dimensional data, since it is difficult to develop good groupings without incurring excessive information loss. Our work aims to overcome these challenges by proposing a hybrid approach, combining se
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ArchiveSafe: Mass-Leakage-Resistant Storage from Proof-of-Workts of system behaviour under different file sizes and puzzle difficulty levels. Our keyless encryption technique can be added as a layer . traditional encryption: together they provide strong security against adversaries without the key and resistance against mass decryption by an attacker.
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