skip to main content


This content will become publicly available on July 1, 2025

Title: Federated Learning on Distributed and Encrypted Data for Smart Manufacturing
Abstract

Industry 4.0 drives exponential growth in the amount of operational data collected in factories. These data are commonly distributed and stored in different business units or cooperative companies. Such data-rich environments increase the likelihood of cyber attacks, privacy breaches, and security violations. Also, this poses significant challenges on analytical computing on sensitive data that are distributed among different business units. To fill this gap, this article presents a novel privacy-preserving framework to enable federated learning on siloed and encrypted data for smart manufacturing. Specifically, we leverage fully homomorphic encryption (FHE) to allow for computation on ciphertexts and generate encrypted results that, when decrypted, match the results of mathematical operations performed on the plaintexts. Multilayer encryption and privacy protection reduce the likelihood of data breaches while maintaining the prediction performance of analytical models. Experimental results in real-world case studies show that the proposed framework yields superior performance to reduce the risk of cyber attacks and harness siloed data for smart manufacturing.

 
more » « less
Award ID(s):
2302834
PAR ID:
10511038
Author(s) / Creator(s):
;
Publisher / Repository:
ASME
Date Published:
Journal Name:
Journal of Computing and Information Science in Engineering
Volume:
24
Issue:
7
ISSN:
1530-9827
Page Range / eLocation ID:
071007-1-12
Subject(s) / Keyword(s):
data privacy fully homomorphic encryption federated learning sustainable manufacturing cyber physical security for factories cybermanufacturing data-driven engineering engineering informatics
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Advanced sensing and cloud systems propel the rapid advancements of service-oriented smart manufacturing. As a result, there is widespread generation and proliferation of data in the interest of manufacturing analytics. The sheer amount and velocity of data have also attracted a myriad of malicious parties, unfortunately resulting in an elevated prevalence of cyber-attacks whose impacts are only gaining in severity. Therefore, this article presents a new distributed cryptosystem for analytical computing on encrypted data in the manufacturing environment, with a case study on manufacturing resource planning. This framework harmonizes Paillier cryptography with the Alternating Direction Method of Multipliers (ADMM) for decentralized computation on encrypted data. Security analysis shows that the proposed Paillier-ADMM system is resistant to attacks from external threats, as well as privacy breaches from trusted-but-curious third parties. Experimental results show that smart allocation is more cost-effective than the benchmarked deterministic and stochastic policies. The proposed distributed cryptosystem shows strong potential to leverage the distributed data for manufacturing intelligence, while reducing the risk of data breaches. 
    more » « less
  2. Recent studies have shown that several government and business organizations experience huge data breaches. Data breaches increase in a daily basis. The main target for attackers is organization sensitive data which includes personal identifiable information (PII) such as social security number (SSN), date of birth (DOB) and credit card /debit card (CCDC). The other target is encryption/decryption keys or passwords to get access to the sensitive data. The cloud computing is emerging as a solution to store, transfer and process the data in a distributed location over the Internet. Big data and internet of things (IoT) increased the possibility of sensitive data exposure. Most methods used for the attack are hacking, unauthorized access, insider theft and false data injection on the move. Most of the attacks happen during three different states of data life cycle such as data-at-rest, data-in-use, and data-in-transit. Hence, protecting sensitive data at all states particularly when data is moving to cloud computing environment needs special attention. The main purpose of this research is to analyze risks caused by data breaches, personal and organizational weaknesses to protect sensitive data and privacy. The paper discusses methods such as data classification and data encryption at different states to protect personal and organizational sensitive data. The paper also presents mathematical analysis by leveraging the concept of birthday paradox to demonstrate the encryption key attack. The analysis result shows that the use of same keys to encrypt sensitive data at different data states make the sensitive data less secure than using different keys. Our results show that to improve the security of sensitive data and to reduce the data breaches, different keys should be used in different states of the data life cycle. 
    more » « less
  3. In the era of cloud computing and big data analysis, how to efficiently share and utilize medical information scattered across various care providers has become a critical problem. This paper proposes a new framework for sharing medical data in a secure and privacy-preserving way. This framework holistically integrates multi-authority attribute based encryption, blockchain and smart contract, as well as software defined networking to define and enforce sharing policies. Specifically in our framework, patients' medical records are encrypted and stored in hospital databases, where strict access controls are enforced with attribute based encryption coupled with privacy level classification. Our framework leverages blockchain technology to connect scattered private databases from participating hospitals for efficient and secure data provision, smart contracts to enable the business logic of clinical data usage, and software defined networking to revoke sharing privileges. The performance evaluation of our prototype demonstrates that the associated computation costs are reasonable in practice. 
    more » « less
  4. —Searchable encryption has received a significant attention from the research community with various constructions being proposed, each achieving asymptotically optimal complexity for specific metrics (e.g., search, update). Despite their elegance, the recent attacks and deployment efforts have shown that the optimal asymptotic complexity might not always imply practical performance, especially if the application demands high privacy. In this article, we introduce a novel Dynamic Searchable Symmetric Encryption (DSSE) framework called Incidence Matrix (IM)-DSSE, which achieves a high level of privacy, efficient search/update, and low client storage with actual deployments on real cloud settings. We harness an incidence matrix along with two hash tables to create an encrypted index, on which both search and update operations can be performed effectively with minimal information leakage. This simple set of data structures surprisingly offers a high level of DSSE security while achieving practical performance. Specifically, IM-DSSE achieves forward-privacy, backward-privacy, and size-obliviousness simultaneously. We also create several DSSE variants, each offering different trade-offs that are suitable for different cloud applications and infrastructures. We fully implemented our framework and evaluated its performance on a real cloud system (Amazon EC2). We have released IM-DSSE as an open-source library for wide development and adaptation. 
    more » « less
  5. In recent years, semiconductor industry has out-sourced the manufacturing to low-cost but not necessarily trusted foundries. This fabless business model encounters new security challenges, including piracy and overproduction. A well-studied solution to prevent unauthorized products from functioning is logic encryption, where a chip is encrypted using a key only known to the designer. However, the majority of the logic encryption solutions are vulnerable due to key uniformity and probing attacks. In this paper, we first present GSAT, a Global attack on existing IC-specific logic encryption schemes using the SAT model, that effectively deciphers the hidden global key pluggable to all the encrypted ICs. Next, we propose a highly secure and low-cost remedy called SPLEnD: Strong PUF -based Logic Encryption Design. Traditional I C-specific encryption schemes are vulnerable to GSAT attack, while SPLEnD not only effectively resists GSAT, but also balances security and efficiency. 
    more » « less