# Privacy-Preserving Technologies in Data

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/privacy-preserving-technologies-in-data/

## Facts

| Field | Value |
| --- | --- |
| Description | 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. |
| Domain | Physical Sciences |
| Field | Computer Science |
| OpenAlex ID | t10764 |
| Works | 90 |

## Topic papers all

Showing 15 of 90.

- [Swarm Learning for decentralized and confidential clinical machine learning](https://scholariq.org/papers/swarm-learning-for-decentralized-and-confidential-clinical-machine-learning/)
- [The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment](https://scholariq.org/papers/the-national-covid-cohort-collaborative-n3c-rationale-design-infrastructure-and/)
- [Website fingerprinting in onion routing based anonymization networks](https://scholariq.org/papers/website-fingerprinting-in-onion-routing-based-anonymization-networks/)
- [Federated learning enables big data for rare cancer boundary detection](https://scholariq.org/papers/federated-learning-enables-big-data-for-rare-cancer-boundary-detection/)
- [Non-invasive prenatal testing for aneuploidy and beyond: challenges of responsible innovation in prenatal screening](https://scholariq.org/papers/non-invasive-prenatal-testing-for-aneuploidy-and-beyond-challenges-of/)
- [A Review on the State-of-the-Art Privacy-Preserving Approaches in the e-Health Clouds](https://scholariq.org/papers/a-review-on-the-state-of-the-art-privacy-preserving-approaches-in-the-e-health/)
- [A Clustering Approach for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm](https://scholariq.org/papers/a-clustering-approach-for-the-mml-math-xmlns-mml-http-www-w3-org-1998-math/)
- [Secure Logistic Regression Based on Homomorphic Encryption: Design and Evaluation](https://scholariq.org/papers/secure-logistic-regression-based-on-homomorphic-encryption-design-and-evaluation/)
- [FedProc: Prototypical contrastive federated learning on non-IID data](https://scholariq.org/papers/fedproc-prototypical-contrastive-federated-learning-on-non-iid-data/)
- [Understanding Malicious Behavior in Crowdsourcing Platforms](https://scholariq.org/papers/understanding-malicious-behavior-in-crowdsourcing-platforms/)
- [Blockchain for Edge of Things: Applications, Opportunities, and Challenges](https://scholariq.org/papers/blockchain-for-edge-of-things-applications-opportunities-and-challenges/)
- [Privacy-Preserving Patient Similarity Learning in a Federated Environment: Development and Analysis](https://scholariq.org/papers/privacy-preserving-patient-similarity-learning-in-a-federated-environment/)
- [Aspects of privacy for electronic health records](https://scholariq.org/papers/aspects-of-privacy-for-electronic-health-records/)
- [Data Sharing System Integrating Access Control Mechanism using Blockchain-Based Smart Contracts for IoT Devices](https://scholariq.org/papers/data-sharing-system-integrating-access-control-mechanism-using-blockchain-based/)
- [Review on security of federated learning and its application in healthcare](https://scholariq.org/papers/review-on-security-of-federated-learning-and-its-application-in-healthcare/)

## Topic primary papers

Showing 15 of 43.

- [A Clustering Approach for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm](https://scholariq.org/papers/a-clustering-approach-for-the-mml-math-xmlns-mml-http-www-w3-org-1998-math/)
- [FedProc: Prototypical contrastive federated learning on non-IID data](https://scholariq.org/papers/fedproc-prototypical-contrastive-federated-learning-on-non-iid-data/)
- [Aspects of privacy for electronic health records](https://scholariq.org/papers/aspects-of-privacy-for-electronic-health-records/)
- [Review on security of federated learning and its application in healthcare](https://scholariq.org/papers/review-on-security-of-federated-learning-and-its-application-in-healthcare/)
- [A Probabilistic Approach for Cooperative Computation Offloading in MEC-Assisted Vehicular Networks](https://scholariq.org/papers/a-probabilistic-approach-for-cooperative-computation-offloading-in-mec-assisted/)
- [Federated learning for IoT devices: Enhancing TinyML with on-board training](https://scholariq.org/papers/federated-learning-for-iot-devices-enhancing-tinyml-with-on-board-training/)
- [PFA: Privacy-preserving Federated Adaptation for Effective Model Personalization](https://scholariq.org/papers/pfa-privacy-preserving-federated-adaptation-for-effective-model-personalization/)
- [A multicenter random forest model for effective prognosis prediction in collaborative clinical research network](https://scholariq.org/papers/a-multicenter-random-forest-model-for-effective-prognosis-prediction-in/)
- [PMC](https://scholariq.org/papers/pmc/)
- [VANTAGE6: an open source priVAcy preserviNg federaTed leArninG infrastructurE for Secure Insight eXchange.](https://scholariq.org/papers/vantage6-an-open-source-privacy-preserving-federated-learning-infrastructure-for/)
- [Location Privacy Challenges in Mobile Edge Computing: Classification and Exploration](https://scholariq.org/papers/location-privacy-challenges-in-mobile-edge-computing-classification-and/)
- [Advanced methods for missing values imputation based on similarity learning](https://scholariq.org/papers/advanced-methods-for-missing-values-imputation-based-on-similarity-learning/)
- [PPSS: A privacy-preserving secure framework using blockchain-enabled federated deep learning for Industrial IoTs](https://scholariq.org/papers/ppss-a-privacy-preserving-secure-framework-using-blockchain-enabled-federated/)
- [LocJury: An IBN-Based Location Privacy Preserving Scheme for IoCV](https://scholariq.org/papers/locjury-an-ibn-based-location-privacy-preserving-scheme-for-iocv/)
- [A SURVEY OF PRIVACY-PRESERVING COLLABORATIVE FILTERING SCHEMES](https://scholariq.org/papers/a-survey-of-privacy-preserving-collaborative-filtering-schemes/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.