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Privacy-Preserving Technologies in Data

Privacy-Preserving Technologies in Data is a research topic within Artificial Intelligence. Science Explorer counts 35k research works in it since 1953. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on privacy-preserving techniques for data analysis and machine learning, including topics such as differential privacy, federated learning, k-anonymity, secure computation, and location privacy. The papers explore methods to protect sensitive information while performing data mining, machine learning, and statistical analysis.

  • Differential Privacy
  • Federated Learning
  • k-Anonymity
  • Privacy Preservation
  • Machine Learning
  • Location Privacy
  • Data Mining
  • Anonymization
  • Secure Computation
  • Membership Inference Attacks
Research works
35k
fractional, since 1953
In the world top 10%
3.4k
per year above
Top-10% rate
9.8%
share of its works in the world top 10%
Growth, 2018-21 → 2022-25
+100%
the tick is no change
Largest producer
China
46% of the 10 largest, 2022-now
Privacy-Preserving Technologies in Data

Which countries lead Privacy-Preserving Technologies in Data research?

By volume, China and the United States publish the most (6.1k and 2.5k works in 2022-now).

By volume, 2022-now

  1. 1 China 6.1k works
  2. 2 United States 2.5k works
  3. 3 India 1.9k works
  4. 4 United Kingdom 532 works
  5. 5 Germany 479 works
  6. 6 Australia 449 works
  7. 7 Canada 414 works
  8. 8 South Korea 377 works
  9. 9 Italy 353 works
  10. 10 Japan 352 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 45.5%United States: 18.3%India: 14.0%United Kingdom: 4.0%6 others listed: 18.1%46%largest
China6,086 · 45.5%United States2,453 · 18.3%India1,876 · 14.0%United Kingdom532 · 4.0%6 others listed2,425 · 18.1%

Shares of the rows listed above, not of the whole node.

Which institutions lead Privacy-Preserving Technologies in Data research?

By volume in 2022-now, Beijing University of Posts and Telecommunications publishes the most Privacy-Preserving Technologies in Data research, followed by Chinese Academy of Sciences and Xidian University.

By volume, 2022-now

  1. 1 Beijing University of Posts and TelecommunicationsChina 180 works
  2. 2 Chinese Academy of SciencesChina 173 works
  3. 3 Xidian UniversityChina 155 works
  4. 4 University of Electronic Science and Technology of ChinaChina 108 works
  5. 5 Shanghai Jiao Tong UniversityChina 92 works
  6. 6 Beijing Institute of TechnologyChina 87 works
  7. 7 Zhejiang UniversityChina 86 works
  8. 8 Tsinghua UniversityChina 85 works
  9. 9 Nanyang Technological UniversitySingapore 84 works
  10. 10 Sun Yat-sen UniversityChina 83 works

Who are the leading researchers in Privacy-Preserving Technologies in Data?

The most-cited researchers publishing on Privacy-Preserving Technologies in Data include Zhong Lin Wang, Yoshua Bengio and John P. A. Ioannidis.

  1. 1 Zhong Lin Wang United States 491k citations
  2. 2 Yoshua Bengio Canada 476k citations
  3. 3 John P. A. Ioannidis United States 317k citations
  4. 4 Donald B. Rubin United States 301k citations
  5. 5 Xiaogang Wang China 260k citations
  6. 6 Mark Gerstein United States 250k citations
  7. 7 Li Fei-Fei United States 225k citations
  8. 8 Michael Snyder United States 224k citations
  9. 9 Kai You Wang China 224k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

What are the most cited papers in Privacy-Preserving Technologies in Data?

The most cited paper in Privacy-Preserving Technologies in Data is "k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY" (Latanya Sweeney, 2002), with 8,620 citations. Next come "Calibrating Noise to Sensitivity in Private Data Analysis" (7,243) and "Deep Learning with Differential Privacy" (6,312).

Most cited papers
#PaperReleasedCitations
1 k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY Latanya Sweeney · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems Oct 2002 8,620
2 Calibrating Noise to Sensitivity in Private Data Analysis Cynthia Dwork, Frank McSherry, Kobbi Nissim et al. · Lecture notes in computer science Conference paper 2006 7,243
3 Deep Learning with Differential Privacy Martı́n Abadi, Andy Chu, Ian Goodfellow et al. Conference paperOpen access 24 Oct 2016 6,312
4 Federated Machine Learning Qiang Yang, Yang Liu, Tianjian Chen et al. · ACM Transactions on Intelligent Systems and Technology 28 Jan 2019 6,287
5 Large-Scale Machine Learning with Stochastic Gradient Descent Léon Bottou Book chapter 2010 5,757
All 99 most cited papers, with filters and sorting

Where is Privacy-Preserving Technologies in Data research done?

The largest centres of Privacy-Preserving Technologies in Data research in 2022-now are Beijing (China), Shanghai (China), Nanjing (China) and Xi'an (China). Among places with at least 20 works in it, it is an unusually large share of all research in Fredericton.

Largest cities, 2022-now

  1. 1 BeijingChina 1.2k works
  2. 2 ShanghaiChina 402 works
  3. 3 NanjingChina 364 works
  4. 4 Xi'anChina 356 works
  5. 5 GuangzhouChina 335 works
  6. 6 WuhanChina 248 works
  7. 7 HangzhouChina 230 works
  8. 8 ChennaiIndia 216 works
  9. 9 ChengduChina 208 works
  10. 10 SingaporeSingapore 205 works

Where it is the local speciality

  1. FrederictonCA · 22.7 works9.1×
← less than its size predicts1×more →

Location quotient: how much more of its research is in Privacy-Preserving Technologies in Data than the world average.

See Privacy-Preserving Technologies in Data on the map

Where is the best place to study Privacy-Preserving Technologies in Data?

Among universities, judged by research, Nanyang Technological University, Hong Kong University of Science and Technology and Xidian University score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%10%20%mean 16.9%fractional works in this field (log) →share of work in the world top 10% →Nanyang Technologica…Hong Kong University…Hong Kong Polytechni…Xidian University
above the meannear itbelow it

One dot per university in the first ten of the table below, at the default weights. The upper left is the interesting corner: small places doing unusually strong work.

Excellenceshare of its work in the world top 10%, 2022-now and all time averaged
40%
Specialisationhow much of its work is in this field, counted up to 10× the world share
10%
Sizeits output in this field, on a log scale
30%
Growthoutput growth, counted from -20% to +20%
10%
Internationalshare of its work with a co-author abroad
10%
Each slider sets a weight; the share it gets of the score is beside it.
#UniversityScoreTop 10%SpecialisationWorksGrowth
1Nanyang Technological UniversitySingapore 74.418.5%7.3×84 +112.3%
2Hong Kong University of Science and TechnologyChina 72.216.6%11.2×67 +157.5%
3Xidian UniversityChina 67.712.1%14.7×155 +54.2%
4Hong Kong Polytechnic UniversityChina 67.115.6%4.1×52 +255.6%
5Singapore Management UniversitySingapore 66.620.0%13.6×21 +33.0%
6Tsinghua UniversityChina 66.018.4%2.5×85 +71.6%
7University of Technology SydneyAustralia 65.916.2%7.5×48 +52.7%
8Wuhan UniversityChina 65.819.2%3.5×71 +180.8%
9City University of Hong KongChina 65.515.9%4.6×38 +137.3%
10University of Hong KongChina 65.416.5%3.4×46 +170.8%

Universities only. The score blends excellence (40%), size (30%), specialisation (10%), growth (10%), international reach (10%), 2022-now; growth compares 2018-21 with 2022-25 and counts up to +20%, since a jump from a small base is mostly noise.

Every institution in Privacy-Preserving Technologies in Data, country by country

Is Privacy-Preserving Technologies in Data research growing?

Output in 2022-2025 was 100% higher than in 2018-2021, peaking in 2025. The fastest-growing topics are Privacy-Preserving Technologies in Data.

still counting19801990200020102020
grewheldshrank

The same series as a ribbon: one cell per year, darker for more. The line above answers how much; this answers when.

Its share of all research

Growing output can still be a shrinking share: all of science grows. This is Privacy-Preserving Technologies in Data as a percentage of every work published that year, 0.075% in 2024.

still counting0.050%0.10%Privacy-Preserving Technologies in Data0.080% in 202519801990200020102020

Fractional works of Privacy-Preserving Technologies in Data over all fractional works that year. How the balance of science has shifted.

How much of it reaches the world top 10%

Year by year.

still counting5%10%15%Privacy-Preserving Technologies in Data9.7% in 202520102020

Of each year's works in it, the share that reached the world's 10% most cited for their field and year. A field is the world for its own topics, so there is no other line to hold it against.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.

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