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Ask a Better Question, Get a Better Answer A New Approach to Private Data Analysis
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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/cpa.20131openalex-snapshot
For most large underdetermined systems of equations, the minimal 𝓁 1 ‐norm near‐solution approximates the sparsest near‐solution
inferredcites →https://doi.org/10.1002/cpa.20132openalex-snapshot
For most large underdetermined systems of linear equations the minimal 𝓁 1 ‐norm solution is also the sparsest solution
inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/11761679_29openalex-snapshot
Our Data, Ourselves: Privacy Via Distributed Noise Generation
inferredcites →https://doi.org/10.1007/978-3-540-28628-8_32openalex-snapshot
Privacy-Preserving Datamining on Vertically Partitioned Databases
inferredcites →https://doi.org/10.1016/0022-0000(84)90070-9openalex-snapshot
Probabilistic encryption
inferredcites →https://doi.org/10.1109/sfcs.2005.5464411openalex-snapshot
Error correction via linear programming
inferredcites →https://doi.org/10.1145/320613.320616openalex-snapshot
Secure statistical databases with random sample queries
inferredcites →https://doi.org/10.1145/76894.76895openalex-snapshot
Security-control methods for statistical databases: a comparative study
inferredcites →https://doi.org/10.1145/773153.773173openalex-snapshot
Revealing information while preserving privacy
inferredcites →https://doi.org/10.1145/773153.773174openalex-snapshot
Limiting privacy breaches in privacy preserving data mining
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-0-387-70992-5_16openalex-snapshot
Private Data Analysis via Output Perturbation
inferred← citeshttps://doi.org/10.1007/978-1-4614-9242-9_8openalex-snapshot
Practical Distributed Privacy-Preserving Data Analysis at Large Scale
inferred← citeshttps://doi.org/10.1007/978-3-540-85174-5_26openalex-snapshot
New Efficient Attacks on Statistical Disclosure Control Mechanisms
inferred← citeshttps://doi.org/10.1007/978-3-540-85654-2_2openalex-snapshot
Hiding Frequent Patterns under Multiple Sensitive Thresholds
inferred← citeshttps://doi.org/10.1007/978-3-642-23556-6_1openalex-snapshot
Quantifying Privacy Violations
inferred← citeshttps://doi.org/10.1007/978-3-642-35890-6_6openalex-snapshot
Differential Privacy in Tripartite Interaction: A Case Study with Linguistic Minorities in Canada
inferred← citeshttps://doi.org/10.1007/978-981-10-8636-6_31openalex-snapshot
Toward Scalable Anonymization for Privacy-Preserving Big Data Publishing
inferred← citeshttps://doi.org/10.1007/978-981-16-5411-4_5openalex-snapshot
Preserving the Privacy of COVID-19 Infected Patients Data Using a Divergent-Scale Supervised Learning for Publishing the Informative Data
inferred← citeshttps://doi.org/10.1007/978-981-33-4370-2_30openalex-snapshot
Data Privacy in Its Three Forms – A Systematic Review
inferred← citeshttps://doi.org/10.1007/978-981-96-4136-9_35openalex-snapshot
A Review on Recent Advances in Privacy Preserving Data Analytics in Big Data
inferred← citeshttps://doi.org/10.1016/j.is.2019.01.001openalex-snapshot
Privacy preserving serial publication of transactional data
inferred← citeshttps://doi.org/10.1016/j.jksuci.2019.08.006openalex-snapshot
Improved l-diversity: Scalable anonymization approach for Privacy Preserving Big Data Publishing
inferred← citeshttps://doi.org/10.1016/j.tcs.2007.11.003openalex-snapshot
Itemset frequency satisfiability: Complexity and axiomatization
inferred← citeshttps://doi.org/10.1109/compsac.2012.70openalex-snapshot
A Solution for Privacy Protection in MapReduce
inferred← citeshttps://doi.org/10.1145/1516360.1516390openalex-snapshot
Detecting privacy violations in database publishing using disjoint queries
inferred← citeshttps://doi.org/10.1145/1749603.1749605openalex-snapshot
Privacy-preserving data publishing
inferred← citeshttps://doi.org/10.1145/2213556.2213570openalex-snapshot
The power of the dinur-nissim algorithm
inferred← citeshttps://doi.org/10.1145/3342559.3365333openalex-snapshot
A vision for autonomous blockchains backed by secure hardware
inferred← citeshttps://doi.org/10.1145/3349265openalex-snapshot
A Comprehensive Survey on Cloud Data Mining (CDM) Frameworks and Algorithms
inferred← citeshttps://doi.org/10.18122/b2fd7fopenalex-snapshot
Privacy-Preserving Trajectory Data Publishing via Differential Privacy
inferred← citeshttps://doi.org/10.4018/978-1-7998-8954-0.ch031openalex-snapshot
Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization
inferred← citeshttps://doi.org/10.4018/ijwp.2019070102openalex-snapshot
Ameliorating the Privacy on Large Scale Aviation Dataset by Implementing MapReduce Multidimensional Hybrid k-Anonymization
inferred← citeshttps://doi.org/10.48550/arxiv.1801.01618openalex-snapshot
Enabling Strong Database Integrity using Trusted Execution Environments
inferred← citeshttps://doi.org/10.5555/1855711.1855731openalex-snapshot
Airavat: security and privacy for MapReduce
inferred← citeshttps://doi.org/10.5626/jcse.2013.7.3.177openalex-snapshot
Differential Privacy in Practice
inferred← citeshttps://doi.org/10.7939/r3s14vopenalex-snapshot
Security Issues in Heterogeneous Data Federations
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