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5,430 results · page 174 of 227
Sébastien Canard, Adel Hamdi, Fabien Laguillaumie
No abstract is available for this record.
Xiangbin Xian, Zhenguo Yang, Guipeng Zhang, Tucua Miro de Nelio S. · 5 authors
No abstract is available for this record.
Aditya Damodaran, Alfredo Rial
No abstract is available for this record.
Jayamine Alupotha, Xavier Boyen, Ernest Foo
No abstract is available for this record.
Qiang Yang, Yang Liu, Yong Cheng, Yan Kang · 6 authors
No abstract is available for this record.
Zhaohua Chen, Guang Yang
Custody is a core financial service in which the custodian holds in safekeeping assets on behalf of the client. Although traditional custody service is typically endorsed by centralized authorities, decentralized custody scheme has become technically feasible since the emergence of digital assets, and furthermore it is badly needed by new applications such as blockchain and DeFi (Decentralized Finance). In this work, we propose a framework of decentralized asset custody scheme that is able to support a large number of custodians and safely hold customer assets of multiple times value of the total security deposit. The proposed custody scheme distributes custodians and assets into many custodian groups via combinatorial designs and random sampling, where each group fully controls the assigned assets. Since every custodian group is small, the overhead cost is significantly reduced. The liveness is also improved because even a single alive group would be able to process transactions. The security of this custody scheme is guaranteed in the game-theoretic sense, such that any adversary corrupting a bounded fraction of custodians cannot move assets more than his own security deposit. We further analyze the security and performance of our constructions, and give explicit examples with concrete numbers and figures for a better understanding of our results.
Wang Jiming, Xueshuo Xie, Yaozheng Fang, Ye Lu · 6 authors
No abstract is available for this record.
Joongi Hong, Suntae Kim, Duksan Ryu
No abstract is available for this record.
Nadia Pocher
This research paper focuses on the interconnections between traditional and cutting-edge technological features of virtual currencies and the EU legal framework to prevent the misuse of the financial system for money laundering and terrorist financing purposes. It highlights a set of Anti-Money Laundering and Counter-Terrorist Financing (AML/CFT) challenges brought about in the Internet of Money (IoM) landscape by the double-edged nature of Distributed Ledger Technologies (DLTs) as both transparency and privacy ori- ented. Special attention is paid to inferences from concepts such as pseudonymity and traceability; this contribution explores these notions by relating them to privacy enhanc- ing mechanisms and blockchain intelligence strategies, while heeding both core elements of the present AML/CFT obliged entities’ framework and possible new conceptualizations. Finally, it identifies key controversies and open questions as to the actual feasibility of ef- fectively applying the “active cooperation” AML/CFT approach to the crypto ecosystems.
Nasim Al Goni, Sherif Saad Ahmed, Ahmed Ibrahim
No abstract is available for this record.
Fabrice Benhamouda, Huijia Lin
No abstract is available for this record.
Miaomiao Zhang, Steven Romero
No abstract is available for this record.
Dakshita Khurana, Muhammad Haris Mughees
No abstract is available for this record.
Daniel Bosk, Simon Bouget, Sonja Buchegger
No abstract is available for this record.
Shuichi Katsumata, Ryo Nishimaki, Shota Yamada, Takashi Yamakawa
No abstract is available for this record.
R. Carlsson
There is a potential in the field of medicine and finance of doing collaborative machine learning. These areas gather data which can be used for developing machine learning models that could predict all from sickness in patients to acts of economical crime like fraud. The problem that exists is that the data collected is mostly of confidential nature and should be handled with precaution. This makes the standard way of doing machine learning - gather data at one centralized server - unwanted to achieve. The safety of the data have to be taken into account. In this project we will explore the Federated learning approach of ”bringing the code to the data, instead of data to the code”. It is a decentralized way of doing machine learning where models are trained on connected devices and data is never shared. Keeping the data privacypreserved.
Norbert Jastroch
No abstract is available for this record.
Omar El Rifai, Maëlle Biotteau, X. De Boissezon, Imen Megdiche · 6 authors
No abstract is available for this record.
Hasventhran Baskaran, Salman Yussof, Fiza Abdul Rahim
No abstract is available for this record.
А. В. Богданов, Alexander Degtyarev, Nadezhda Shchegoleva, Valery Khvatov
No abstract is available for this record.
Fahad Ahmed Al-Zahrani
In modern times, many individuals, businesses and the Internet of Things (IoT) integrated industries collect huge amounts of meaningful data daily, which may be beneficial for other individuals and businesses as well. By utilizing this data, future trends to make the right decisions on the bases of facts and figures are analyzed efficiently. In addition to that, many new ways are paved for researchers to utilize this data in their upcoming research. However, due to some major issues like security, privacy and access control of data, data owners avoid sharing data among themselves. Another main problem is the selfish behavior of data owners. Businesses also act selfishly and invest huge amounts of money to collect and maintain the data for their benefits. Therefore, data owners are hesitant to share their data with others without the availability of a fair profit and secure data-sharing platform. Moreover, consumers are not much motivated to buy data from Data Providers (DPs) due to its bad quality and inconsistency. The data provided by data owners is mostly incomplete, outdated, heterogeneous and costly. In this paper, a subscription-based data-sharing model is proposed by leveraging the blockchain technology and Data as a Service (DaaS) concept. In this model, users subscribe to a DP for a specific period to get access to the data and pay according to the subscription plan. The DP keeps receiving revenue recurrently for a long-time, which has a huge profit margin in comparison with selling data at once. Furthermore, two major pricing models, Flat Rate Pricing (FRP) and Usage-Based Pricing (UBP), are discussed to set standards for data owners to monetize their data, and a new hybrid pricing model is also proposed. Blockchain technology is utilized in the proposed model to make it secure, transparent and immutable. To investigate the performance of the proposed model, a private blockchain network is deployed using a web interface provided by MultiChain blockchain. The simulation results demonstrate that the proposed model is feasible and efficient. The theoretical discussion proves that the proposed model is beneficial for both data owners and data consumers and has a good scope in the future for data management and trading processes.
Rémi Clarisse, Olivier Sanders
No abstract is available for this record.
Michele Ciampi, Roberto Parisella, Daniele Venturi
We study adaptive security of delayed-input Sigma protocols and non-interactive zero-knowledge (NIZK) proof systems in the common reference string (CRS) model. Our contributions are threefold: We exhibit a generic compiler taking any delayed-input Sigma protocol and returning a delayed-input Sigma protocol satisfying adaptive-input special honest-verifier zero knowledge (SHVZK). In case the initial Sigma protocol also satisfies adaptive-input special soundness, our compiler preserves this property. We revisit the recent paradigm by Canetti et al. (STOC 2019) for obtaining NIZK proof systems in the CRS model via the Fiat-Shamir transform applied to so-called trapdoor Sigma protocols, in the context of adaptive security. In particular, assuming correlation-intractable hash functions for all sparse relations, we prove that Fiat-Shamir NIZKs satisfy either: (i) Adaptive soundness (and non-adaptive zero knowledge), so long as the challenge is obtained by hashing both the prover’s first round and the instance being proven; (ii) Adaptive zero knowledge (and non-adaptive soundness), so long as the challenge is obtained by hashing only the prover’s first round, and further assuming that the initial trapdoor Sigma protocol satisfies adaptive-input SHVZK. We exhibit a generic compiler taking any Sigma protocol and returning a trapdoor Sigma protocol. Unfortunately, this transform does not preserve the delayed-input property of the initial Sigma protocol (if any). To complement this result, we also give yet another compiler taking any delayed-input trapdoor Sigma protocol and returning a delayed-input trapdoor Sigma protocol with adaptive-input SHVZK.
Shadan Ghaffaripour, Ali Miri
No abstract is available for this record.