CoChat
Sign Up
Cited by 111Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in DataEthics and Social Impacts of AI

The DOI matches its Crossref record; 69 of the 71 references it cites were found in a registry or an index.

Privacy and Security of Big Data in AI Systems: A Research and Standards Perspective

Open access
Dilmaghani, Saharnaz, Brust, Matthias R., Danoy, Gregoire, Cassagnes, Natalia, Pecero, Johnatan, Bouvry, Pascal·2019·2019 IEEE International Conference on Big Data (Big Data)
DOI 10.1109/bigdata47090.2019.9006283
Open this paper in CoChat — free account

Abstract

The huge volume, variety, and velocity of big data have empowered Machine Learning (ML) techniques and Artificial Intelligence (AI) systems. However, the vast portion of data used to train AI systems is sensitive information. Hence, any vulnerability has a potentially disastrous impact on privacy aspects and security issues. Nevertheless, the increased demands for high-quality AI from governments and companies require the utilization of big data in the systems. Several studies have highlighted the threats of big data on different platforms and the countermeasures to reduce the risks caused by attacks. In this paper, we provide an overview of the existing threats which violate privacy aspects and security issues inflicted by big data as a primary driving force within the AI/ML workflow. We define an adversarial model to investigate the attacks. Additionally, we analyze and summarize the defense strategies and countermeasures of these attacks. Furthermore, due to the impact of AI systems in the market and the vast majority of business sectors, we also investigate Standards Developing Organizations (SDOs) that are actively involved in providing guidelines to protect the privacy and ensure the security of big data and AI systems. Our far-reaching goal is to bridge the research and standardization frame to increase the consistency and efficiency of AI systems developments guaranteeing customer satisfaction while transferring a high degree of trustworthiness.

Overview

Integrity signals

Registry

Crossref · 10.1109/bigdata47090.2019.9006283

Cites

69 of 69 references verified0 citation errors · 0 not checkable

Statistics

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

Verification

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?

Citations

Cites

Show all 71 references

Research works printed but not linked: 16

  1. wall, 2019, Biased and wrong? facial recognition tech in the dock
  2. 0, Iso/iec cd 20547-4 Information technology – big data reference architecture – part 4 Security and privacy
  3. 2017, Open algorithms
  4. 2018, Iso/iec tr 20547-2: Information technology – big data reference architecture – part 2: Use cases and derived requirements, International Organization for Standardization
  5. laney, 2001, 3d data management: Controlling data volume, velocity and variety, META Group Research Note, 6
  6. 0, Iso/iec wd 23053 Framework for artificial intelligence (ai) systems using machine learning (ml)
  7. 0, Iso/iec wd 22989 Artificial intelligence – concepts and terminology
  8. larson, 2016, How we analyzed the compas recidivism algorithm, ProPublica
  9. tramer, 2016, Stealing‘ machine learning models via prediction apis, 25th USENIX Security Symposium
  10. 0, Iso/iec np tr 24027 Information technology – artificial intelligence (ai) – bias in ai systems and ai aided decision making
  11. cihon, 2019, Standards for ai governance International standards to enable global coordination in ai research & development
  12. 2019, Artificial intelligence blockchain and distributed ledger technologies
  13. 0, Iso/iec pd tr 24028 Information technology – artificial intelligence (ai) – overview of trustworthiness in artificial intelligence
  14. keith kirkpatrick, 2018, Artificial intelligence use cases
  15. aditya, 2016, Artificial intelligence market forecasts
  16. vincent, 2016, Twitter taught microsoft’s ai chatbot to be a racist asshole in less than a day

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Dilmaghani, S., Brust, M. R., Danoy, G., Cassagnes, N., Pecero, J., & Bouvry, P.
Affiliations
University of Luxembourg (LU)
ORCID
Venue
2019 IEEE International Conference on Big Data (Big Data)
Publisher
IEEE
Published
2019-12
Pages
5737-5743
DOI
10.1109/bigdata47090.2019.9006283
OpenAlex
W3008060249
Cited by
111
References
71
Citations by year
2026: 12 · 2025: 31 · 2024: 32 · 2023: 16 · 2022: 11 · 2021: 4 · 2020: 5
FWCI
1.91
Type
conference paper
Language
en
Open access
Licence
Locations
  • IEEE International Conference on Big Data (BigData) (publishedVersion)
  • Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
Keywords
big data privacy, big data security, artificial intelligence systems, machine learning, adversarial attacks, attack countermeasures, privacy-preserving machine learning, standards developing organizations, privacy standards, sensitive data, trustworthiness

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.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review