On the public citation graph

De-pseudonymization of Smart Metering Data: Analysis and Countermeasures

https://doi.org/10.1109/giots.2018.8534430
0 signed · 23 inferred · view as graph

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.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00779-012-0513-6openalex-snapshot
Re-identification of Smart Meter data
inferredcites →https://doi.org/10.1016/j.is.2015.06.006openalex-snapshot
Individual privacy constraints on time-series data
inferredcites →https://doi.org/10.1109/5.192069openalex-snapshot
Nonintrusive appliance load monitoring
inferredcites →https://doi.org/10.1109/access.2015.2506198openalex-snapshot
DEP2SA: A Decentralized Efficient Privacy-Preserving and Selective Aggregation Scheme in Advanced Metering Infrastructure
inferredcites →https://doi.org/10.1109/jsyst.2013.2260940openalex-snapshot
Toward Unified Security and Privacy Protection for Smart Meter Networks
inferredcites →https://doi.org/10.1109/mpae.2003.1192027openalex-snapshot
Power signature analysis
inferredcites →https://doi.org/10.1109/smartgrid.2010.5622050openalex-snapshot
Smart Grid Privacy via Anonymization of Smart Metering Data
inferredcites →https://doi.org/10.1109/smartgridcomm.2015.7436272openalex-snapshot
How the quantity and quality of training data impacts re-identification of smart meter users?
inferredcites →https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.55openalex-snapshot
Deploying and Evaluating Pufferfish Privacy for Smart Meter Data
inferredcites →https://doi.org/10.1145/2076732.2076764openalex-snapshot
Smart metering de-pseudonymization
inferredcites →https://doi.org/10.1145/2517840.2517844openalex-snapshot
Analysis of the impact of data granularity on privacy for the smart grid
inferredcites →https://doi.org/10.1145/2751323.2751325openalex-snapshot
A study on data de-pseudonymization in the smart grid

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.4483407openalex-extracted
Privacy-Preserving Local Energy Markets: A Systematic Literature Review
inferred← citeshttps://doi.org/10.1007/s10207-022-00612-8openalex-snapshot
DFTMicroagg: a dual-level anonymization algorithm for smart grid data
inferred← citeshttps://doi.org/10.1007/s12083-024-01709-8openalex-snapshot
Consortium blockchain based secure and efficient data aggregation and dynamic billing system in smart grid
inferred← citeshttps://doi.org/10.1109/access.2022.3157390openalex-snapshot
Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation
inferred← citeshttps://doi.org/10.1109/jiot.2020.2983213openalex-snapshot
Smart Meter Data Obfuscation Using Correlated Noise
inferred← citeshttps://doi.org/10.1109/sefet65155.2025.11255465openalex-snapshot
Obfuscating Smart Meter Data Using Differential Privacy with Post-processing
inferred← citeshttps://doi.org/10.1109/tsg.2019.2906016openalex-snapshot
A Secure and Privacy-Preserving Protocol for Smart Metering Operational Data Collection
inferred← citeshttps://doi.org/10.1126/sciadv.abl6464openalex-snapshot
Expanding the attack surface: Robust profiling attacks threaten the privacy of sparse behavioral data
inferred← citeshttps://doi.org/10.1145/3777518.3777533openalex-snapshot
Inferring the Hidden: Privacy Risks of Microaggregation in Smart Meter Data
inferred← citeshttps://doi.org/10.32920/21408597openalex-snapshot
Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation
inferred← citeshttps://doi.org/10.32920/21408597.v1openalex-snapshot
Evaluation of Noise Distributions for Additive and Multiplicative Smart Meter Data Obfuscation
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.