Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

Olivera Kotevska; Wenjun Yang; Eyhab Al-Masri

doi:10.1109/ICCD65941.2025.00128

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

Olivera Kotevska
, Wenjun Yang
, Eyhab Al-Masri

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

The rapid expansion of distributed and edge computing platforms - spanning autonomous vehicles, IoT sensors, and healthcare monitors - has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.

Original language	English
Title of host publication	Proceedings - 2025 IEEE 43rd International Conference on Computer Design, ICCD 2025
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	872-875
Number of pages	4
ISBN (Electronic)	9798331503468
DOIs	https://doi.org/10.1109/ICCD65941.2025.00128
State	Published - 2025
Event	43rd International Conference on Computer Design, ICCD 2025 - Richardson, United States Duration: Nov 10 2025 → Nov 12 2025

Publication series

Name	Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors
ISSN (Print)	1063-6404

Conference

Conference	43rd International Conference on Computer Design, ICCD 2025
Country/Territory	United States
City	Richardson
Period	11/10/25 → 11/12/25

Funding

This material is based upon work co-supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research under Contract No. DE-AC05- 00OR22725. This manuscript has been co-authored by UTBattelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doepublic-access-plan).

Keywords

Differential Privacy
Edge Computing
Federated Learning
IoT
Secure Systems
System Architecture

Access to Document

10.1109/ICCD65941.2025.00128

Cite this

Kotevska, O., Yang, W., & Al-Masri, E. (2025). Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures. In Proceedings - 2025 IEEE 43rd International Conference on Computer Design, ICCD 2025 (pp. 872-875). (Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCD65941.2025.00128

Kotevska, Olivera ; Yang, Wenjun ; Al-Masri, Eyhab. / Engineering Privacy at the Edge : A Practical Guide to Differential Privacy in System Architectures. Proceedings - 2025 IEEE 43rd International Conference on Computer Design, ICCD 2025. Institute of Electrical and Electronics Engineers Inc., 2025. pp. 872-875 (Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors).

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title = "Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures",

abstract = "The rapid expansion of distributed and edge computing platforms - spanning autonomous vehicles, IoT sensors, and healthcare monitors - has heightened concerns about data privacy. Differential Privacy (DP) offers a rigorous mathematical framework to protect sensitive information while retaining analytical utility. This tutorial introduces the foundations of DP for both numerical and categorical datasets and extends the discussion to correlation-aware techniques tailored for structured and high-dimensional data. Hands-on demonstrations will begin with the PETINA (Privacy prEservaTIoN Algorithms) package for numerical data and continue with MIC-DP (Maximum Information Correlated Differential Privacy) for tabular data. Designed for researchers and practitioners in secure systems, embedded architectures, and AI accelerators, the tutorial emphasizes practical and scalable methods for integrating DP into real-world system designs.",

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doi = "10.1109/ICCD65941.2025.00128",

language = "English",

series = "Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors",

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Kotevska, O, Yang, W & Al-Masri, E 2025, Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures. in Proceedings - 2025 IEEE 43rd International Conference on Computer Design, ICCD 2025. Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors, Institute of Electrical and Electronics Engineers Inc., pp. 872-875, 43rd International Conference on Computer Design, ICCD 2025, Richardson, United States, 11/10/25. https://doi.org/10.1109/ICCD65941.2025.00128

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures. / Kotevska, Olivera; Yang, Wenjun; Al-Masri, Eyhab.
Proceedings - 2025 IEEE 43rd International Conference on Computer Design, ICCD 2025. Institute of Electrical and Electronics Engineers Inc., 2025. p. 872-875 (Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Kotevska O, Yang W, Al-Masri E. Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures. In Proceedings - 2025 IEEE 43rd International Conference on Computer Design, ICCD 2025. Institute of Electrical and Electronics Engineers Inc. 2025. p. 872-875. (Proceedings - IEEE International Conference on Computer Design: VLSI in Computers and Processors). doi: 10.1109/ICCD65941.2025.00128

Engineering Privacy at the Edge: A Practical Guide to Differential Privacy in System Architectures

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