Cited by 144Privacy-Preserving Technologies in DataCryptography and Data SecurityCloud Data Security Solutions

Privacy-Preserving Decision Trees over Vertically Partitioned Data

Vaidya, Jaideep, Clifton, Chris·2005·Lecture Notes in Computer Science

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1007/11535706_11

Cites

21 of 21 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • The vertical FL setting requires a more complex mechanism to decompose the loss function at each party than horizontal FL.

    “Unlike sample-partitioned/horizontal FL, the vertical FL setting requires a more complex mechanism to decompose the loss function at each party [5], [15], [16].”

    From SecureBoost: A Lossless Federated Learning Framework (2021) · cites this paper as [16]

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Cited by

Sorted by citations

Authors
Vaidya, J., & Clifton, C.
Affiliations
  • Rutgers, The State University of New Jersey (US)
  • Purdue University West Lafayette (US)
Venue
Lecture Notes in Computer Science
Published
2005
Pages
139-152
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
OpenAlex
W2679684481
Cited by
144
References
21
Citations by year
2026: 1 · 2025: 2 · 2024: 4 · 2023: 3 · 2022: 8 · 2021: 6 · 2020: 7 · 2019: 10 · 2018: 5 · 2017: 10 · 2016: 8 · 2015: 7 · 2014: 8 · 2013: 10 · 2012: 10
FWCI
5.23
Type
conference paper
Language
en
Open access
closed
Locations
Lecture notes in computer science (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Artificial Intelligence › Cryptography and Data Security
  • Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
Keywords
privacy-preserving data mining, decision tree, ID3, vertically partitioned data, distributed knowledge discovery, classification, information-theoretic security, multi-party data sharing

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