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The limits of differential privacy (and its misuse in data release and machine learning)
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1073/pnas.2003714117openalex-snapshot
How differential privacy will affect our understanding of health disparities in the United States
inferredcites →https://doi.org/10.1109/bigdata47090.2019.9005465openalex-snapshot
Federated Learning with Bayesian Differential Privacy
inferredcites →https://doi.org/10.1109/tifs.2020.2988575openalex-snapshot
Federated Learning With Differential Privacy: Algorithms and Performance Analysis
inferredcites →https://doi.org/10.1145/1989323.1989345openalex-snapshot
No free lunch in data privacy
inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot
Deep Learning with Differential Privacy
inferredcites →https://doi.org/10.1145/3287287openalex-snapshot
Understanding database reconstruction attacks on public data
inferredcites →https://doi.org/10.1145/3291276.3295691openalex-snapshot
Understanding Database Reconstruction Attacks on Public Data
inferredcites →https://doi.org/10.1257/pandp.20191107openalex-snapshot
Differential Privacy and Census Data: Implications for Social and Economic Research
inferredcites →https://doi.org/10.1561/9781601988195openalex-snapshot
The Algorithmic Foundations of Differential Privacy
inferredcites →https://doi.org/10.48550/arxiv.2002.04049openalex-snapshot
Guidelines for Implementing and Auditing Differentially Private Systems
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.7020openalex-snapshot
(k, m, t)‐anonymity: Enhanced privacy for transactional data
inferred← citeshttps://doi.org/10.1007/978-3-030-89811-3_19openalex-snapshot
Inferring the Meaning of Non-personal, Anonymized, and Anonymous Data
inferred← citeshttps://doi.org/10.1007/978-3-030-96896-0_13openalex-snapshot
Protecting Against Data Leakage in Federated Learning: What Approach Should You Choose?
inferred← citeshttps://doi.org/10.1007/978-3-031-13448-7_7openalex-snapshot
Geolocated Data Generation and Protection Using Generative Adversarial Networks
inferred← citeshttps://doi.org/10.1007/978-3-031-13945-1_13openalex-snapshot
Generation of Synthetic Trajectory Microdata from Language Models
inferred← citeshttps://doi.org/10.1007/978-3-031-13945-1_7openalex-snapshot
On Privacy of Multidimensional Data Against Aggregate Knowledge Attacks
inferred← citeshttps://doi.org/10.1007/978-3-031-30637-2_20openalex-snapshot
Accurate and Efficient Trajectory-Based Contact Tracing with Secure Computation and Geo-Indistinguishability
inferred← citeshttps://doi.org/10.1007/978-3-031-66047-4_6openalex-snapshot
Privacy-Enhancing Technologies
inferred← citeshttps://doi.org/10.1007/978-3-031-69651-0_14openalex-snapshot
An Examination of the Alleged Privacy Threats of Confidence-Ranked Reconstruction of Census Microdata
inferred← citeshttps://doi.org/10.1007/978-3-031-88708-6_4openalex-snapshot
Measuring Actual Privacy of Obfuscated Queries in Information Retrieval
inferred← citeshttps://doi.org/10.1007/s00170-026-18585-6openalex-snapshot
Reverse engineering of part geometries and machining process parameters from CNC machining time series measurements
inferred← citeshttps://doi.org/10.1007/s00440-024-01279-zopenalex-snapshot
Private measures, random walks, and synthetic data
inferred← citeshttps://doi.org/10.1007/s10207-022-00607-5openalex-snapshot
A systematic overview on methods to protect sensitive data provided for various analyses
inferred← citeshttps://doi.org/10.1007/s10207-025-01169-yopenalex-snapshot
A survey on privacy of health data lifecycle: a taxonomy, review, and future directions
inferred← citeshttps://doi.org/10.1007/s10462-024-10970-5openalex-snapshot
Federated learning-based natural language processing: a systematic literature review
inferred← citeshttps://doi.org/10.1007/s10489-022-03435-1openalex-snapshot
Explaining predictions and attacks in federated learning via random forests
inferred← citeshttps://doi.org/10.1007/s10586-023-04063-1openalex-snapshot
A privacy-enhanced human activity recognition using GAN & entropy ranking of microaggregated data
inferred← citeshttps://doi.org/10.1007/s10618-021-00778-0openalex-snapshot
Differentially Private Distance Learning in Categorical Data
inferred← citeshttps://doi.org/10.1007/s11113-023-09829-4openalex-snapshot
Assessing the Impact of Differential Privacy on Population Uniques in Geographically Aggregated Data: The Case of the 2020 U.S. Census
inferred← citeshttps://doi.org/10.1007/s11113-024-09931-1openalex-snapshot
Protecting Vulnerable Respondents: A Critical Analysis of the Privacy-Preserving Methods of the 2010 and 2020 Decennial Census
inferred← citeshttps://doi.org/10.1007/s13042-024-02134-2openalex-snapshot
Meta-DPSTL: meta learning-based differentially private self-taught learning
inferred← citeshttps://doi.org/10.1007/s13042-025-02910-8openalex-snapshot
Optimizing k-anonymity with automated generalization trees: a study on classification utility
inferred← citeshttps://doi.org/10.1016/b978-0-44-323761-4.00010-9openalex-snapshot
The fundamentals of AI ethics in medical imaging
inferred← citeshttps://doi.org/10.1016/b978-0-443-26629-4.00170-2openalex-snapshot
Data confidentiality, differential privacy, and statistical disclosure control
inferred← citeshttps://doi.org/10.1016/j.comcom.2022.04.011openalex-snapshot
Decentralized k -anonymization of trajectories via privacy-preserving tit-for-tat
inferred← citeshttps://doi.org/10.1016/j.comnet.2021.108621openalex-snapshot
Fairness, integrity, and privacy in a scalable blockchain-based federated learning system
inferred← citeshttps://doi.org/10.1016/j.compag.2024.109557openalex-snapshot
Security threats to agricultural artificial intelligence: Position and perspective
inferred← citeshttps://doi.org/10.1016/j.cose.2021.102488openalex-snapshot
k -Anonymity in practice: How generalisation and suppression affect machine learning classifiers
inferred← citeshttps://doi.org/10.1016/j.engappai.2021.104468openalex-snapshot
Achieving security and privacy in federated learning systems: Survey, research challenges and future directions
inferred← citeshttps://doi.org/10.1016/j.future.2023.06.010openalex-snapshot
Differential privacy in deep learning: Privacy and beyond
inferred← citeshttps://doi.org/10.1016/j.future.2024.01.009openalex-snapshot
Benchmarking robustness and privacy-preserving methods in federated learning
inferred← citeshttps://doi.org/10.1016/j.future.2024.06.043openalex-snapshot
Enabling federated learning across the computing continuum: Systems, challenges and future directions
inferred← citeshttps://doi.org/10.1016/j.ins.2022.09.004openalex-snapshot
A decision-support framework for data anonymization with application to machine learning processes
inferred← citeshttps://doi.org/10.1016/j.ipm.2024.103929openalex-snapshot
PRoT-FL: A privacy-preserving and robust Training Manager for Federated Learning
inferred← citeshttps://doi.org/10.1016/j.isci.2022.105331openalex-snapshot
Synthetic data as an enabler for machine learning applications in medicine
inferred← citeshttps://doi.org/10.1016/j.jnca.2022.103465openalex-snapshot
Revealing the landscape of privacy-enhancing technologies in the context of data markets for the IoT: A systematic literature review
inferred← citeshttps://doi.org/10.1016/j.knosys.2024.112945openalex-snapshot
Enhancing text anonymization via re-identification risk-based explainability
inferred← citeshttps://doi.org/10.1016/j.mlwa.2026.100881openalex-snapshot
Generative BlendPose-LDM: Utility-preserving face image anonymization via k-anonymous latent diffusion
inferred← citeshttps://doi.org/10.1016/j.patter.2023.100913openalex-snapshot
Learning across diverse biomedical data modalities and cohorts: Challenges and opportunities for innovation
inferred← citeshttps://doi.org/10.1017/s0003055422001411openalex-snapshot
Statistically Valid Inferences from Privacy-Protected Data
inferred← citeshttps://doi.org/10.1073/pnas.2104906119openalex-snapshot
Balancing data privacy and usability in the federal statistical system
inferred← citeshttps://doi.org/10.1080/24694452.2023.2178377openalex-snapshot
A Computational Framework for Preserving Privacy and Maintaining Utility of Geographically Aggregated Data: A Stochastic Spatial Optimization Approach
inferred← citeshttps://doi.org/10.1093/jamia/ocac011openalex-snapshot
Dynamically adjusting case reporting policy to maximize privacy and public health utility in the face of a pandemic
inferred← citeshttps://doi.org/10.1108/jeet-04-2023-0006openalex-snapshot
Understanding algorithm bias in artificial intelligence-enabled ERP software customization
inferred← citeshttps://doi.org/10.1109/access.2021.3124309openalex-snapshot
Differential Privacy for IoT-Enabled Critical Infrastructure: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1109/access.2024.3368852openalex-snapshot
A Taxonomy of Syntactic Privacy Notions for Continuous Data Publishing
inferred← citeshttps://doi.org/10.1109/access.2024.3388992openalex-snapshot
Secure Multi-Party Computation for Machine Learning: A Survey
inferred← citeshttps://doi.org/10.1109/access.2024.3510332openalex-snapshot
Solving the Privacy-Equity Trade-off in Data Sharing By Using Homophily, Diversity, and t-Closeness Based Anonymity Algorithm
inferred← citeshttps://doi.org/10.1109/bibm66473.2025.11356719openalex-snapshot
PFL-MD: A Privacy-Preserving Federated Learning Framework for Melanoma Diagnosis with Multiple Party Fully Homomorphic Encryption
inferred← citeshttps://doi.org/10.1109/csf68417.2026.00007openalex-snapshot
Privacy Mechanism Design based on Empirical Distributions
inferred← citeshttps://doi.org/10.1109/csr54599.2022.9850328openalex-snapshot
Privacy Preserving Human Activity Recognition Using Microaggregated Generative Deep Learning
inferred← citeshttps://doi.org/10.1109/hpcc64274.2024.00074openalex-snapshot
FAdagrad: Adaptive federated learning with differential privacy
inferred← citeshttps://doi.org/10.1109/icaiccit64383.2024.10912324openalex-snapshot
A Comprehensive Analysis of Various Federated Learning Techniques
inferred← citeshttps://doi.org/10.1109/icbc56567.2023.10174943openalex-snapshot
BDSP: A Fair Blockchain-enabled Framework for Privacy-Enhanced Enterprise Data Sharing
inferred← citeshttps://doi.org/10.1109/icct-europe63283.2025.11157698openalex-snapshot
Evaluating the Effectiveness of Differential Privacy Against Profiling
inferred← citeshttps://doi.org/10.1109/icscds65426.2025.11167481openalex-snapshot
Privacy-Preserving Data Mining Techniques in Big Data Environments
inferred← citeshttps://doi.org/10.1109/mc.2024.3515412openalex-snapshot
Synthetic Data: A New Frontier for Democratizing Artificial Intelligence and Data Access
inferred← citeshttps://doi.org/10.1109/mci.2023.3327892openalex-snapshot
When Evolutionary Computation Meets Privacy
inferred← citeshttps://doi.org/10.1109/msec.2022.3188365openalex-snapshot
Rectification of Syntactic and Semantic Privacy Mechanisms
inferred← citeshttps://doi.org/10.1109/percomworkshops51409.2021.9431047openalex-snapshot
Usable Differential Privacy for the Internet-of-Things
inferred← citeshttps://doi.org/10.1109/pst58708.2023.10320176openalex-snapshot
A Secure Distributed Learning Framework Using Homomorphic Encryption
inferred← citeshttps://doi.org/10.1109/ssp64130.2025.11073431openalex-snapshot
Stochastic Mixing and Differential Privacy in Utility-Preserving Data Anonymization
inferred← citeshttps://doi.org/10.1109/tdsc.2024.3525149openalex-snapshot
Synthetic Data Generation via the Permutation Paradigm With Optional k-Anonymity
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3599911openalex-snapshot
Dual-Server Privacy-Preserving Collaborative Deep Learning: A Round-Efficient, Dynamic and Lossless Approach
inferred← citeshttps://doi.org/10.1109/tdsc.2025.3627244openalex-snapshot
Enhancing Security and Privacy in Multi-Server Federated Learning
inferred← citeshttps://doi.org/10.1109/tetc.2023.3258528openalex-snapshot
Towards Optimization of Privacy-Utility Trade-Off Using Similarity and Diversity Based Clustering
inferred← citeshttps://doi.org/10.1109/tfuzz.2022.3157385openalex-snapshot
Fuzzy Differential Privacy Theory and Its Applications in Subgraph Counting
inferred← citeshttps://doi.org/10.1109/tifs.2022.3228753openalex-snapshot
A New Global Measure to Simultaneously Evaluate Data Utility and Privacy Risk
inferred← citeshttps://doi.org/10.1109/tkde.2026.3673144openalex-snapshot
A Sensitivity-Aware and PSO-Driven Differential Privacy Method With Customized Budgets for Structured Data Perturbation
inferred← citeshttps://doi.org/10.1109/tnnls.2022.3212627openalex-snapshot
Enhanced Security and Privacy via Fragmented Federated Learning
inferred← citeshttps://doi.org/10.1145/3528580.3535331openalex-snapshot
Ethics by Design in Decentralized Computing
inferred← citeshttps://doi.org/10.1145/3535101openalex-snapshot
Graph Neural Networks in Recommender Systems: A Survey
inferred← citeshttps://doi.org/10.1145/3544548.3580950openalex-snapshot
Emotion AI at Work: Implications for Workplace Surveillance, Emotional Labor, and Emotional Privacy
inferred← citeshttps://doi.org/10.1145/3547139openalex-snapshot
A Critical Review on the Use (and Misuse) of Differential Privacy in Machine Learning
inferred← citeshttps://doi.org/10.1145/3548606.3560693openalex-snapshot
Am I Private and If So, how Many?
inferred← citeshttps://doi.org/10.1145/3560816openalex-snapshot
Blockchain-Based Federated Learning for Securing Internet of Things: A Comprehensive Survey
inferred← citeshttps://doi.org/10.1145/3569452openalex-snapshot
Studying the Impact of Data Disclosure Mechanism in Recommender Systems via Simulation
inferred← citeshttps://doi.org/10.1145/3617694.3623256openalex-snapshot
Unraveling the Interconnected Axes of Heterogeneity in Machine Learning for Democratic and Inclusive Advancements
inferred← citeshttps://doi.org/10.1145/3627703.3650082openalex-snapshot
DeTA: Minimizing Data Leaks in Federated Learning via Decentralized and Trustworthy Aggregation
inferred← citeshttps://doi.org/10.1145/3636424openalex-snapshot
A Survey of Generative Adversarial Networks for Synthesizing Structured Electronic Health Records
inferred← citeshttps://doi.org/10.1145/3637309openalex-snapshot
Communicating the Privacy-Utility Trade-off: Supporting Informed Data Donation with Privacy Decision Interfaces for Differential Privacy
inferred← citeshttps://doi.org/10.1146/annurev-statistics-033121-112921openalex-snapshot
Statistical Data Privacy: A Song of Privacy and Utility
inferred← citeshttps://doi.org/10.1155/2023/2959503openalex-snapshot
A Normative Approach to Privacy‐Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms
inferred← citeshttps://doi.org/10.1177/0282423x241235259openalex-snapshot
Steps Toward a Shared Infrastructure for Multi-Party Secure Private Computing in Official Statistics
inferred← citeshttps://doi.org/10.1177/0282423x241312023openalex-snapshot
Statistical Disclosure Control: Moving Forward
inferred← citeshttps://doi.org/10.1186/s12942-022-00300-9openalex-snapshot
Reconciling public health common good and individual privacy: new methods and issues in geoprivacy
inferred← citeshttps://doi.org/10.1186/s13635-025-00203-9openalex-snapshot
Differential privacy and artificial intelligence: potentials, challenges, and future avenues
inferred← citeshttps://doi.org/10.1214/24-aoas2009openalex-snapshot
A privacy-preserved and high-utility synthesis strategy for risk-based stratified subgroups of the Canadian scleroderma patient registry data
inferred← citeshttps://doi.org/10.1371/journal.pone.0271714openalex-snapshot
Predicting age and gender from network telemetry: Implications for privacy and impact on policy
inferred← citeshttps://doi.org/10.14569/ijacsa.2022.0130266openalex-snapshot
Evaluation of Re-identification Risk using Anonymization and Differential Privacy in Healthcare
inferred← citeshttps://doi.org/10.1609/aaai.v37i12.26771openalex-snapshot
Conflicting Interactions among Protection Mechanisms for Machine Learning Models
inferred← citeshttps://doi.org/10.18653/v1/2021.acl-long.323openalex-snapshot
Anonymisation Models for Text Data: State of the art, Challenges and Future Directions
inferred← citeshttps://doi.org/10.2139/ssrn.4525320openalex-snapshot
Advancing Privacy in Deep Learning Through Data Transformations
inferred← citeshttps://doi.org/10.2196/34472openalex-snapshot
Privacy of Study Participants in Open-access Health and Demographic Surveillance System Data: Requirements Analysis for Data Anonymization
inferred← citeshttps://doi.org/10.2196/preprints.34472openalex-snapshot
Privacy of study participants in open-access Health and Demographic Surveillance System data: A requirements analysis for data anonymisation (Preprint)
inferred← citeshttps://doi.org/10.2478/jos-2023-0017openalex-snapshot
Database Reconstruction Is Not So Easy and Is Different from Reidentification
inferred← citeshttps://doi.org/10.25300/misq/2025/18502openalex-snapshot
Shapley Value-Based Feature Attribution for Data Masking
inferred← citeshttps://doi.org/10.3390/app12157408openalex-snapshot
The Protection of Data Sharing for Privacy in Financial Vision
inferred← citeshttps://doi.org/10.3390/app14145975openalex-snapshot
Challenges of Using Synthetic Data Generation Methods for Tabular Microdata
inferred← citeshttps://doi.org/10.3390/electronics12091978openalex-snapshot
A Generic Approach towards Enhancing Utility and Privacy in Person-Specific Data Publishing Based on Attribute Usefulness and Uncertainty
inferred← citeshttps://doi.org/10.48550/arxiv.2108.01521openalex-snapshot
Bit-efficient Numerical Aggregation and Stronger Privacy for Trust in Federated Analytics
inferred← citeshttps://doi.org/10.56553/popets-2023-0045openalex-snapshot
Lessons Learned: Surveying the Practicality of Differential Privacy in the Industry
inferred← citeshttps://doi.org/10.64628/aai.p5jhprxvkopenalex-snapshot
Here’s how machine learning can violate your privacy
inferred← citeshttps://doi.org/10.70675/9719f9b2za9f9z4e0fza756ze63dcf2f6b32openalex-snapshot
Addressing interpretability fairness & privacy in machine learning through combinatorial optimization methods
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