Jul 2026· Applied and Computational Engineering· Vol 231, pp. 118-124· 0 citations
TL;DR
A lightweight collaborative geographic privacy preservation mechanism for dynamic data streams called Pruning-CGP, which has blocked side-channel cardinality leakage and can be applied to address the demand for a high-performance, low-resource algorithm in the context of edge computing.
Abstract
There are serious problems of privacy protection for continuous spatial data streams in the edge computing scenario. Traditional Concentrated Geo-Privacy (CGP) mechanisms and the existing adaptive methods both have a utility-efficiency bottleneck. Due to high concurrency and resource limitations in the environment, the current model has a large computational cost, and local spatial pruning methods often overlook the risk of cardinality leakage to deduce the true density. In response to the aforementioned weaknesses, this paper puts forward a lightweight collaborative geographic privacy preservation mechanism for dynamic data streams called Pruning-CGP. First, a physically separated query architecture will separate data ingestion from real-time queries to reduce computation. Second, in the zero-concentrated Differential Privacy (zCDP) setting, a discrete Gaussian mechanism is used for cardinality-preserving noise addition. In conjunction with the public prior, an adaptive distribution of the privacy budget is realised. Extensive physical stress tests with actual data have shown that the engineering feasibility of the proposed architecture has been realised. Under the above-mentioned privacy-equivalent constraints, the system has blocked side-channel cardinality leakage. Although it has a relatively low recall rate of about 31.5% and keeps the distance error at a practical hundred-meter level (e.g., 62 meters), it also achieves an extremely fast sub-millisecond response speed of 0.16 ms. Based on the above results, the Pruning-CGP mechanism can be applied to address the demand for a high-performance, low-resource algorithm in the context of edge computing.
In autonomous data exchange environments, where data is flowing in real-time, it is crucial to have comprehensive security solutions to ensure user privacy and system performance. This paper presents a Dynamic Privacy Preservation Architecture specifically developed for untrusted, decentralised networks of autonomous nodes that frequently interact. The framework uses adaptive anonymisation algorithms and decentralised trust verification mechanisms to dynamically protect private data tokens based on context sensitivity and the recipient's risk profile. The evaluation of this architecture was conducted through an experimental study using a synthetic dataset from the operational Internet of Things network, comprising 159 distinct communications. Using the Python programming language and Python-specific data science packages such as Pandas for data manipulation, Scikit-learn for metric evaluation, and Matplotlib for visual plotting, system performance, computational overhead and privacy metrics were simulated and analysed. The results show that the proposed architecture is suitable to optimise the data utility-privacy trade-off in absolute terms while preserving strict privacy. The system dynamically scales its defence mechanisms to keep processing latencies low and effectively prevent unauthorised reconstruction attacks, thereby providing a reliable, secure and highly scalable ecosystem for autonomous data stakeholders.
Chaitanya Bharath Somineni· FMDB Transactions on Sustain...· 0 citations
With the rapid expansion of edge computing, vast volumes of sensitive data are now being generated and processed at the network's periphery, raising significant concerns about privacy and data security. Federated Analytics (FA) emerges as a transformative solution by enabling decentralized data analysis without the need to transfer raw data to central servers, thereby mitigating potential privacy breaches. This study investigates the integration of FA into edge computing ecosystems, leveraging advanced Privacy-Enhancing Technologies (PETs) such as Differential Privacy (DP), Secure Multiparty Computation (SMC), and Homomorphic Encryption (HE) to ensure robust privacy protections. A multi-layered architecture is proposed and evaluated using simulations on Raspberry Pi clusters and synthetic workload datasets to emulate real-world edge environments. Experimental results indicate that FA, especially when combined with DP, achieves a strong balance between analytical accuracy and computational efficiency, while SMC and HE offer enhanced security at the cost of increased computational overhead. The findings underscore the practicality and effectiveness of FA for privacy-preserving analytics at the edge, suggesting its potential to support compliance with data protection regulations and meet the demands of future applications. The paper concludes by emphasizing the need for further research in optimizing scalability, minimizing resource usage, and exploring synergies with emerging technologies such as 6G and intelligent orchestration platforms to fully realize the promise of federated edge analytics.
John McCarthy, M. Minsky· International Journal of Dat...· 0 citations
The Privacy Index is introduced, a unified metric designed to aggregate multiple privacy-related factors into a single, interpretable score that integrates the validation of established privacy models, detection of anonymization techniques, and estimation of re-identification risk under different attacker assumptions.
José A. Gameiro, J. Oliveira, João Rafael Almeida· International Conference on...· 0 citations
Local Differential Privacy (LDP) provides strong privacy guarantees for collecting numerical data. A fundamental challenge, however, is that existing LDP mechanisms require a predefined data domain, which is often unknown in practice. This lack of prior knowledge creates a critical dilemma for the data collector: if the chosen domain is too narrow, values outside the range are clipped, leading to information loss. Conversely, if the domain is too wide, excessive noise is added during the privatization process, which degrades the quality of collected data. This highlights the need for methods that can dynamically estimate the data domain. In this work, we propose an adaptive LDP framework that addresses this problem. In our method, each user sends two pieces of information: their perturbed numerical data, and a privatized signal indicating if their original value was clipped by the current domain. By aggregating these signals, our proposed method, Adaptive Bounding of Clipping regions (ABC) method, iteratively adjusts the domain to fit the underlying data distribution without prior knowledge. Our theoretical analysis shows that the estimated data domain converges to an appropriate range. In the empirical evaluation, the results demonstrate that our framework significantly improves the quality of numerical data collection across various datasets and underlying LDP mechanisms. We also show that the estimated range successfully converges in practice and our approach is robust to its hyperparameters through comprehensive ablation studies.
Incheol Baek, Hyungbin Kim, Y. Chung· IEEE International Conferenc...· 0 citations
PADP is presented, a sensitivity-aware perturbation framework inspired by differential privacy principles, which provides a plug-and-play, middleware framework that can be easily integrated into enterprise RAG pipelines without requiring costly computations for LLM fine-tuning and reconstruction of vector indices.
Seçkin Mandaci, Yılmaz Vural, Ö. Turna· Black Sea Journal of Enginee...· 0 citations
trasgoDP is a modular, open-source, and easy-to-use Python framework for releasing tabular microdata under {\epsilon}-local differential privacy guarantees, as well as location data under geo-indistinguishability assumptions, designed to be installed and integrated within standard data science workflows. The software enables systematic exploration of privacy-utility trade-offs across multiple mechanisms, data types, and {\epsilon} values. While differential privacy has been extensively studied for aggregate data, its application to row-wise microdata release remains underexploited in terms of reusable software tools, a gap that is even more pronounced in the case of metric privacy and location-based data. trasgoDP implements local-DP mechanisms for numerical and categorical attributes (Laplace, Gaussian, Exponential, and Randomized Response), a geo-indistinguishability mechanism for location data, and a set of utility metrics, including a novel correlation-loss measure, to quantify information loss as a function of the allocated privacy budget. The objective of this work is to provide the research community with a reproducible, open-source baseline for evaluating tabular and location-based data publication methodologies under formal local differential privacy guarantees.
