Cited by 1,772Blockchain Technology Applications and SecurityBlockchain Technology in Education and LearningCryptography and Data Security

A Survey on the Security of Blockchain Systems

Open access
Li, Xiaoqi, Jiang, Peng, Chen, Ting, Luo, Xiapu, Wen, Qiaoyan·2020·Future Generation Computer Systems

Integrity signals

Registry

Crossref · 10.1016/j.future.2017.08.020

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Some blockchain solutions such as Uport or Sovrin deal with private key loss by providing recovery and revocation mechanisms for the private keys.

    “Some blockchain solutions such as Uport [20] or Sovrin [82] deal with this issue by providing recovery and revocation mechanisms for the private keys.”
  • Current blockchain implementations such as uPort or Sovrin are starting or planning to adopt these standards.

    “current blockchain implementations such as uPort [20] or Sovrin [82] are starting or planning to adopt these standards.”
  • With traditional symmetric or asymmetric encryption, the creator of the data would upload the encrypted transaction and then distribute the decryption key off-ledger, or using an instantiation of a decentralized PKI over the blockchain like Sovrin to manage public keys.

    “With traditional symmetric or asymmetric encryption, the creator of the data would upload the encrypted transaction and then distribute the decryption key off-ledger, or using an instantiation IEEEAccess of a decentralized PKI over the blockchain like in Sovrin [82] to manage public keys.”
  • Data threats or adversaries can enter blockchain software to hold control of the blockchain, leading to modifications of medical transaction or patient data and raising privacy concerns.

    “Data threats or adversaries can enter the blockchain software to hold the control of the blockchain, which can lead to serious consequences, like modifications of medical transaction or patient data information, raising privacy concerns [158].”

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Authors
Li, X., Jiang, P., Chen, T., Luo, X., & Wen, Q.
Affiliations
  • Hong Kong Polytechnic University (HK)
  • University of Electronic Science and Technology of China (CN)
  • Beijing University of Posts and Telecommunications (CN)
Corresponding author
Luo, Xiapu
Venue
Future Generation Computer Systems
Publisher
Elsevier BV
Published
2020-06
Volume
107
Pages
841-853
ISSN
0167-739X (ISSN-L)
OpenAlex
W2963264685
Cited by
1,772
References
5
Citations by year
2026: 88 · 2025: 180 · 2024: 178 · 2023: 265 · 2022: 299 · 2021: 284 · 2020: 228 · 2019: 187 · 2018: 60 · 2017: 3
FWCI
243.79
Type
journal article
Language
en
Open access
Locations
  • Future Generation Computer Systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • PolyU Institutional Research Archive (Hong Kong Polytechnic University) (submittedVersion, cc-by-nc-nd)
  • arXiv (Cornell University)
Funders
  • Science and Technology Planning Project of Guangdong Province (2015B020233018)
  • National Natural Science Foundation of China (61471228, 61502086, 61572115, 61402080, 61572109)
  • China Postdoctoral Science Foundation (2014M562307)
Topics
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
  • Physical Sciences › Computer Science › Information Systems › Blockchain Technology in Education and Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
Keywords
blockchain security, security threats, blockchain attacks, smart contracts, cryptocurrency, privacy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

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