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Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11681878_14openalex-snapshot
Calibrating Noise to Sensitivity in Private Data Analysis
inferredcites →https://doi.org/10.1007/978-3-540-79228-4_1openalex-snapshot
Differential Privacy: A Survey of Results
inferredcites →https://doi.org/10.1038/srep01376openalex-snapshot
Unique in the Crowd: The privacy bounds of human mobility
inferredcites →https://doi.org/10.1080/01621459.1965.10480775openalex-snapshot
Randomized Response: A Survey Technique for Eliminating Evasive Answer Bias
inferredcites →https://doi.org/10.1109/icdcs.2011.79openalex-snapshot
SpotME If You Can: Randomized Responses for Location Obfuscation on Mobile Phones
inferredcites →https://doi.org/10.1109/icde.2006.1openalex-snapshot
L-diversity: privacy beyond k-anonymity
inferredcites →https://doi.org/10.1109/icde.2007.367856openalex-snapshot
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
inferredcites →https://doi.org/10.1109/icde.2008.4497446openalex-snapshot
Never Walk Alone: Uncertainty for Anonymity in Moving Objects Databases
inferredcites →https://doi.org/10.1109/icde.2012.16openalex-snapshot
Differentially Private Spatial Decompositions
inferredcites →https://doi.org/10.1109/icde.2015.7113387openalex-snapshot
PrivGeoCrowd: A toolbox for studying private spatial Crowdsourcing
inferredcites →https://doi.org/10.1109/icdm.2016.0169openalex-snapshot
Differential Location Privacy for Sparse Mobile Crowdsensing
inferredcites →https://doi.org/10.1109/infocom.2014.6848002openalex-snapshot
Achieving k-anonymity in privacy-aware location-based services
inferredcites →https://doi.org/10.1109/iwqos.2016.7590444openalex-snapshot
Your trajectory privacy can be breached even if you walk in groups
inferredcites →https://doi.org/10.1109/mprv.2003.1186725openalex-snapshot
Location privacy in pervasive computing
inferredcites →https://doi.org/10.1109/percom.2009.4912864openalex-snapshot
Towards privacy-sensitive participatory sensing
inferredcites →https://doi.org/10.1109/percom.2013.6526720openalex-snapshot
Privacy preservation for participatory sensing data
inferredcites →https://doi.org/10.1109/tmc.2016.2586058openalex-snapshot
Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing
inferredcites →https://doi.org/10.1109/wowmom.2013.6583387openalex-snapshot
Experiences with performance tradeoffs in practical, continuous indoor localization
inferredcites →https://doi.org/10.1145/1066116.1189037openalex-snapshot
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
inferredcites →https://doi.org/10.1145/1401890.1401926openalex-snapshot
Composition attacks and auxiliary information in data privacy
inferredcites →https://doi.org/10.1145/2031331.2031335openalex-snapshot
Trajectory privacy in location-based services and data publication
inferredcites →https://doi.org/10.1145/2031331.2031337openalex-snapshot
A privacy-aware framework for participatory sensing
inferredcites →https://doi.org/10.1145/275487.275508openalex-snapshot
Generalizing data to provide anonymity when disclosing information (abstract)
inferredcites →https://doi.org/10.1145/2818048.2819995openalex-snapshot
Campus-Scale Mobile Crowd-Tasking
inferredcites →https://doi.org/10.1145/2971648.2971741openalex-snapshot
Location privacy for crowdsourcing applications
inferredcites →https://doi.org/10.14778/2732951.2732966openalex-snapshot
A framework for protecting worker location privacy in spatial crowdsourcing
inferredcites →https://doi.org/10.5555/1182635.1164193openalex-snapshot
The new Casper: query processing for location services without compromising privacy
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119545293.ch8openalex-snapshot
Crowdsourcing and Human‐in‐the‐Loop for IoT
inferred← citeshttps://doi.org/10.1002/cpe.5100openalex-snapshot
A differentially private method for crowdsourcing data submission
inferred← citeshttps://doi.org/10.1007/s41060-020-00207-3openalex-snapshot
(So) Big Data and the transformation of the city
inferred← citeshttps://doi.org/10.1016/j.cose.2025.104473openalex-snapshot
Privacy preservation techniques through data lifecycle: A comprehensive literature survey
inferred← citeshttps://doi.org/10.1016/j.jbusres.2021.01.030openalex-snapshot
Role of group cohesiveness in targeted mobile promotions
inferred← citeshttps://doi.org/10.1016/j.jisa.2022.103204openalex-snapshot
PGAN framework for synthesizing sensor data privately
inferred← citeshttps://doi.org/10.1016/j.sysarc.2019.01.005openalex-snapshot
P2TA: Privacy-preserving task allocation for edge computing enhanced mobile crowdsensing
inferred← citeshttps://doi.org/10.1145/3287045openalex-snapshot
Crowd-enabled Processing of Trustworthy, Privacy-Enhanced and Personalised Location Based Services with Quality Guarantee
inferred← citeshttps://doi.org/10.1145/3366423.3380106openalex-snapshot
Privacy-preserving AI Services Through Data Decentralization
inferred← citeshttps://doi.org/10.1145/3403931openalex-snapshot
Mapping Points of Interest Through Street View Imagery and Paid Crowdsourcing
inferred← citeshttps://doi.org/10.1145/3478106openalex-snapshot
The Crowd Wisdom for Location Privacy of Crowdsensing Photos
inferred← citeshttps://doi.org/10.1145/3806015openalex-snapshot
QualitEye: Public and Privacy-preserving Gaze Data Quality Verification ETRA001
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Science and Advanced Computing — runs a structured editorial panel for researchers
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