Oct 2026· Proceedings on Privacy Enhancing Technologies· 0 citations· 41 references
TL;DR
SyntheGrAnon is introduced, a framework for evaluating synthetic graph anonymity that primarily targets the singling out, linkability, and inference risks outlined in the EU GDPR at the node and community levels, while also including edge-level attacks as an extension of the node-level setting.
Abstract
Graphs are a ubiquitous form of structured data, with applications in many privacy-sensitive domains, such as social and healthcare. As for other modalities, modern graph synthesizers enable the creation of realistic synthetic samples, facilitating privacy-preserving data sharing while maintaining high utility. Unfortunately, unlike such other modalities, there is no relevant work on evaluating the privacy risk associated with synthetic graphs. The fact that graphs, unlike, e.g., tables, naturally capture relationships between individuals means that existing approaches are not easily transferable. To allow quantifying these privacy risks, we introduce SyntheGrAnon, a framework for evaluating synthetic graph anonymity. SyntheGrAnon primarily targets the singling out, linkability, and inference risks outlined in the EU GDPR at the node and community levels, while also including edge-level attacks as an extension of the node-level setting. We design attacks tailored to synthetic graphs and, in addition, extend the existing methodology by leveraging multiple synthetic samples for our black-box attacks. In our evaluation, spanning datasets from social and financial domains and five generative graph models, including three modern diffusion-based options, we find that our attacks are mostly effective, achieving risks close to the maximum of 1 in some cases. However, they struggle with large-scale, attribute-scarce graphs.
This paper studies embedding-space privacy as a representation-level learning problem. Rather than altering raw records directly, the proposed framework applies embeddingspace transformation to full-record representations through Gaussian perturbation and adversarial representation sanitization. The method is evaluated through ablation across utility metrics, linkage attacks, attribute-inference attacks, and membership-inference tests. The primary empirical evaluation uses a synthetic fusion recommendation benchmark built from MovieLens [1], [2] 32M behavior and Adult-derived demographics [3], while a secondary synthetic medical benchmark is used to examine cross-domain transferability under more constrained conditions. The strongest results appear in the recommendation experiments. Under grouped demographic privacy evaluation, the combined condition preserves recommendation utility with $N D C G {@} K=0.6312$ while reducing exact and entity linkage from 0.7090/0.7204 to 0.0001/0.0000. Sensitive-target attacker performance remains near the majority baseline, supporting the claim of empirical privacy improvement without visible ranking degradation in that benchmark. The healthcare experiments also demonstrate meaningful embedding transformation and linkage reduction, though the current benchmark remains datalimited and therefore less conclusive for utility-focused evaluation. Overall, the findings support the conclusion that embeddingspace transformation can preserve downstream utility while substantially reducing linkage risk and sensitive-information recoverability under explicit attacker evaluation. The findings support embedding-space transformation as a practical privacypreserving strategy for embedding-driven AI systems under explicit attacker evaluation.
D. Panagoulias, Evangelia-Aikaterini Tsichrintzi, E. Sakkopoulos· International Conference on...· 0 citations
Initial evaluations using various machine-learning algorithms on pre-and post-generalized datasets demonstrate the privacy framework’s effectiveness in mitigating privacy risks while preserving data usability.
Ze-Yang Zhu, Matthias N. Louws, Roland V. Bumbuc et al.· 0 citations
PPGNN, a personalized differentially private framework for decentralized graph data, enables user-specific privacy budgets during local perturbation while preserving analytical utility in decentralized graph learning scenarios.
Longzhu He, Peng Tang, Chaozhuo Li et al.· IEEE Transactions on Knowled...· 0 citations
This paper empirically evaluates ChatGPT 3.5 and 4.0 using over 23,000 real user-generated medical queries, assessing their susceptibility to privacy breaches through quasi-identifiers such as age, location, phone number and national registration number and proposes a scalable privacy evaluation model that combines k-anonymity, l-diversity, t-closeness, entropy, re-identification risk and delta-disclosure.
Foad Jalali, Mehran Alidoost Nia· Journal of Supercomputing· 0 citations
A novel metric, ``Dependency Triad''(DT), is proposed, which summarizes the pairwise dependency information relevant to CPL using three parameters and yields a conservative estimator of pairwise CPL, which is particularly suitable for high-cardinality attributes.
Sandaru Jayawardana, S. Ulukus, Ming Ding et al.· 0 citations
This research introduces a Decision Tree-based synthetic data generation method, DTSDG and evaluates its performance using six well-known anonymization techniques, providing the best balance between privacy preservation and the data utility.
Dhananjay M. Kanade, Dr. Shirish S. Sane, Dr. Uday Wad· Journal of Intelligent Decis...· 0 citations
