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Differentially Private K-Means Clustering and a Hybrid Approach to Private Optimization
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/asi.21234openalex-snapshot
Introduction to Information Retrieval
inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/978-3-540-28628-8_32openalex-snapshot
Privacy-Preserving Datamining on Vertically Partitioned Databases
inferredcites →https://doi.org/10.1007/s00357-006-0018-yopenalex-snapshot
Generation of Random Clusters with Specified Degree of Separation
inferredcites →https://doi.org/10.1016/s0167-8655(99)00069-0openalex-snapshot
An empirical comparison of four initialization methods for the K-Means algorithm
inferredcites →https://doi.org/10.1109/focs.2007.41openalex-snapshot
Towards Sharp Inapproximability For Any 2-CSP
inferredcites →https://doi.org/10.1109/icde.2012.16openalex-snapshot
Differentially Private Spatial Decompositions
inferredcites →https://doi.org/10.1109/icde.2013.6544872openalex-snapshot
Differentially private grids for geospatial data
inferredcites →https://doi.org/10.1109/tit.1982.1056489openalex-snapshot
Least squares quantization in PCM
inferredcites →https://doi.org/10.1109/tkde.2010.247openalex-snapshot
Differential Privacy via Wavelet Transforms
inferredcites →https://doi.org/10.1111/1467-9868.00293openalex-snapshot
Estimating the Number of Clusters in a Data Set Via the Gap Statistic
inferredcites →https://doi.org/10.1137/1.9781611973440.68openalex-snapshot
Towards Accurate Histogram Publication under Differential Privacy
inferredcites →https://doi.org/10.1145/1250790.1250803openalex-snapshot
Smooth sensitivity and sampling in private data analysis
inferredcites →https://doi.org/10.1145/1557019.1557090openalex-snapshot
Differentially private recommender systems
inferredcites →https://doi.org/10.1145/1835804.1835868openalex-snapshot
Data mining with differential privacy
inferredcites →https://doi.org/10.1145/1835804.1835869openalex-snapshot
Discovering frequent patterns in sensitive data
inferredcites →https://doi.org/10.1145/1866739.1866758openalex-snapshot
A firm foundation for private data analysis
inferredcites →https://doi.org/10.1145/1993636.1993743openalex-snapshot
Privacy-preserving statistical estimation with optimal convergence rates
inferredcites →https://doi.org/10.1145/2857705.2857708openalex-snapshot
Differentially Private K-Means Clustering
inferredcites →https://doi.org/10.1145/2882903.2882931openalex-snapshot
Principled Evaluation of Differentially Private Algorithms using DPBench
inferredcites →https://doi.org/10.1145/773153.773173openalex-snapshot
Revealing information while preserving privacy
inferredcites →https://doi.org/10.14778/1920841.1920970openalex-snapshot
Boosting the accuracy of differentially private histograms through consistency
inferredcites →https://doi.org/10.2172/6985080openalex-snapshot
Interfacing 1990 US Census TIGER map files with New S graphics software. [Topologically Integrated Geographic Encoding and Referencing (TIGER)]
inferredcites →https://doi.org/10.48550/arxiv.1012.4763openalex-snapshot
A simple and practical algorithm for differentially private data release
inferredcites →https://doi.org/10.5860/choice.46-2715openalex-snapshot
Introduction to information retrieval
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.6503openalex-snapshot
Privacy‐preserving mechanism for mixed data clustering with local differential privacy
inferred← citeshttps://doi.org/10.1007/978-3-030-16148-4_47openalex-snapshot
A Convergent Differentially Private k-Means Clustering Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-030-37352-8_16openalex-snapshot
Research on K-Means Clustering Algorithm Over Encrypted Data
inferred← citeshttps://doi.org/10.1007/978-3-030-95388-1_18openalex-snapshot
A High-Availability K-modes Clustering Method Based on Differential Privacy
inferred← citeshttps://doi.org/10.1007/978-3-031-22677-9_35openalex-snapshot
Efficient-Secure k-means Clustering Guaranteeing Personalized Local Differential Privacy
inferred← citeshttps://doi.org/10.1007/978-3-032-23450-6_5openalex-snapshot
BPGT: A Novel Privacy-Preserving K-Means Clustering Framework to Guarantee Local $$d_{\chi }$$-Privacy
inferred← citeshttps://doi.org/10.1007/978-981-97-2387-4_10openalex-snapshot
Coordinate Descent for k-Means with Differential Privacy
inferred← citeshttps://doi.org/10.1007/s00778-017-0492-3openalex-snapshot
PrivPfC: differentially private data publication for classification
inferred← citeshttps://doi.org/10.1007/s10618-021-00778-0openalex-snapshot
Differentially Private Distance Learning in Categorical Data
inferred← citeshttps://doi.org/10.1007/s11042-023-18067-xopenalex-snapshot
Improving search result clustering using nature inspired approach
inferred← citeshttps://doi.org/10.1007/s11280-022-01088-1openalex-snapshot
Towards Understanding the fairness of differentially private margin classifiers
inferred← citeshttps://doi.org/10.1007/s11280-026-01436-5openalex-snapshot
Trading off privacy, utility, and stability in privacy-preserving K-means clustering using an adaptive multi-objective memetic algorithm
inferred← citeshttps://doi.org/10.1007/s11432-020-2910-1openalex-snapshot
Differential identifiability clustering algorithms for big data analysis
inferred← citeshttps://doi.org/10.1016/b978-0-44-324840-5.00012-3openalex-snapshot
Data safety: defenses
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109808openalex-snapshot
Granular data representation under privacy protection: Tradeoff between data utility and privacy via information granularity
inferred← citeshttps://doi.org/10.1016/j.hcc.2025.100342openalex-snapshot
Secure three-party clustering with identifying malicious behavior
inferred← citeshttps://doi.org/10.1016/j.ins.2025.122417openalex-snapshot
Non-interactive K-mode clustering of high-dimensional categorical data under local differential privacy
inferred← citeshttps://doi.org/10.1016/j.ins.2026.123524openalex-snapshot
Mosaic: Differentially private representation of density-based clustering results
inferred← citeshttps://doi.org/10.1016/j.knosys.2019.02.028openalex-snapshot
Differential privacy preservation in regression analysis based on relevance
inferred← citeshttps://doi.org/10.1016/j.neucom.2021.09.050openalex-snapshot
Practical multi-party private collaborative k-means clustering
inferred← citeshttps://doi.org/10.1016/j.patcog.2024.110890openalex-snapshot
Improving the utility of differentially private clustering through dynamical processing
inferred← citeshttps://doi.org/10.1016/j.sysarc.2020.101940openalex-snapshot
Privacy attacks against deep learning models and their countermeasures
inferred← citeshttps://doi.org/10.1049/iet-ifs.2019.0255openalex-snapshot
Privacy‐preserving constrained spectral clustering algorithm for large‐scale data sets
inferred← citeshttps://doi.org/10.1109/access.2019.2909559openalex-snapshot
Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions
inferred← citeshttps://doi.org/10.1109/icassp48485.2024.10447243openalex-snapshot
On the Privacy of Federated Clustering: a Cryptographic View
inferred← citeshttps://doi.org/10.1109/icde65706.2026.00138openalex-snapshot
CFDGraph: Privacy-Preserving Graph Processing for Large-Scale Collaborative Fraud Detection
inferred← citeshttps://doi.org/10.1109/ispds58840.2023.10235471openalex-snapshot
Multi-Key Clustering Method for Cloud Environments' Privacy-Preserving
inferred← citeshttps://doi.org/10.1109/mci.2020.2976185openalex-snapshot
A Survey on Differentially Private Machine Learning [Review Article]
inferred← citeshttps://doi.org/10.1109/mdm55031.2022.00074openalex-snapshot
Sharing and Generating Privacy-Preserving Spatio-Temporal Data Using Real-World Knowledge
inferred← citeshttps://doi.org/10.1109/nana51271.2020.00022openalex-snapshot
K-modes Based Categorical Data Clustering Algorithms Satisfying Differential Privacy
inferred← citeshttps://doi.org/10.1109/nana63151.2024.00029openalex-snapshot
HiDS Data clustering algorithm based on differential privacy
inferred← citeshttps://doi.org/10.1109/spcom71105.2026.11622906openalex-snapshot
On the Optimal Number of Grids for Differentially Private Non-Interactive $K$-Means Clustering
inferred← citeshttps://doi.org/10.1109/tdsc.2020.3043369openalex-snapshot
Differentially Private k-Means Clustering with Convergence Guarantee
inferred← citeshttps://doi.org/10.1109/tfuzz.2024.3380596openalex-snapshot
Fuzzy Prediction Model in Privacy Protection: Takagi–Sugeno Rules Model via Differential Privacy
inferred← citeshttps://doi.org/10.1109/tifs.2022.3189532openalex-snapshot
K-Means Clustering With Local dᵪ-Privacy for Privacy-Preserving Data Analysis
inferred← citeshttps://doi.org/10.1109/tnse.2023.3337828openalex-snapshot
A Lightweight Mutual Privacy Preserving $k$-Means Clustering in Industrial IoT
inferred← citeshttps://doi.org/10.1109/tpsisa52974.2021.00009openalex-snapshot
A Comparative Evaluation of Differentially Private Image Obfuscation
inferred← citeshttps://doi.org/10.1145/3409289openalex-snapshot
Proactively Identifying Emerging Hacker Threats from the Dark Web
inferred← citeshttps://doi.org/10.1145/3433210.3453104openalex-snapshot
Privacy-preserving Density-based Clustering
inferred← citeshttps://doi.org/10.1145/3469830.3470893openalex-snapshot
Privacy-Preserving Synthetic Location Data in the Real World
inferred← citeshttps://doi.org/10.1145/3698875openalex-snapshot
Co-clustering: A Survey of the Main Methods, Recent Trends, and Open Problems
inferred← citeshttps://doi.org/10.1145/3749161openalex-snapshot
Differentially Private Explanations for Clusters
inferred← citeshttps://doi.org/10.1145/3774426openalex-snapshot
Secure and Dropout-Resilient Three-Party Clustering Based on Cloud-Edge-Client Collaboration
inferred← citeshttps://doi.org/10.1155/2021/5564176openalex-snapshot
Generalized and Multiple-Queries-Oriented Privacy Budget Strategies in Differential Privacy via Convergent Series
inferred← citeshttps://doi.org/10.2478/popets-2020-0080openalex-snapshot
Practical Privacy-Preserving K-means Clustering
inferred← citeshttps://doi.org/10.2478/popets-2021-0068openalex-snapshot
SoK: Efficient Privacy-preserving Clustering
inferred← citeshttps://doi.org/10.26599/bdma.2022.9020050openalex-snapshot
K-Means Clustering with Local Distance Privacy
inferred← citeshttps://doi.org/10.3233/jifs-213564openalex-snapshot
RETRACTED: CVDP k-means clustering algorithm for differential privacy based on coefficient of variation
inferred← citeshttps://doi.org/10.4018/ijssmet.2020040101openalex-snapshot
Identification of Telecom Volatile Customers Using a Particle Swarm Optimized K-Means Clustering on Their Personality Traits Analysis
inferred← citeshttps://doi.org/10.48550/arxiv.2002.01043openalex-snapshot
Differentially Private k-Means Clustering with Guaranteed Convergence
inferred← citeshttps://doi.org/10.54097/ajst.v4i3.4783openalex-snapshot
Differential Privacy-based Personalized Recommendation Service for "Helping Farmers" Tourism in Guizhou Province
inferred← citeshttps://doi.org/10.5539/cis.v14n2p26openalex-snapshot
Efficient and Privacy-Preserving Multi-User Outsourced K-Means Clustering
inferred← citeshttps://doi.org/10.70675/32098958z1deez4f21zb51az429b616621fdopenalex-snapshot
Efficient and privacy-preserving compressive learning
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