TY - GEN
T1 - Privacy-preserving techniques for protecting large-scale data of cyber-physical systems
AU - Keshk, Marwa
AU - Moustafa, Nour
AU - Sitnikova, Elena
AU - Turnbull, Benjamin
AU - Vatsalan, Dinusha
PY - 2020/12
Y1 - 2020/12
N2 - As Cyber-Physical Systems (CPSs), such as power and gas networks, generate heterogeneous and large-scale data sources from devices and networks, they need efficient privacy-preserving techniques to protect data and systems from cyber attacks. To safeguard CPSs from potential cyber threats, it is vital to identify vulnerabilities of CPSs' components to prevent Advanced Persistent Threats (APTs) and protect their generated data using privacy-preserving techniques. This paper aims to review the current state of privacy-preserving techniques for protecting CPSs and their networks against cyber attacks. Concepts of Privacy preservation and CPSs are discussed, illustrating CPSs' components and how they could be hacked using cyber and physical hacking scenarios. Then, types of privacy preservation, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are discussed to demonstrate how they would be applied to protect the original data in CPSs and their networks. Finally, we explain existing challenges, solutions and future research directions of privacy preservation in CPSs.
AB - As Cyber-Physical Systems (CPSs), such as power and gas networks, generate heterogeneous and large-scale data sources from devices and networks, they need efficient privacy-preserving techniques to protect data and systems from cyber attacks. To safeguard CPSs from potential cyber threats, it is vital to identify vulnerabilities of CPSs' components to prevent Advanced Persistent Threats (APTs) and protect their generated data using privacy-preserving techniques. This paper aims to review the current state of privacy-preserving techniques for protecting CPSs and their networks against cyber attacks. Concepts of Privacy preservation and CPSs are discussed, illustrating CPSs' components and how they could be hacked using cyber and physical hacking scenarios. Then, types of privacy preservation, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are discussed to demonstrate how they would be applied to protect the original data in CPSs and their networks. Finally, we explain existing challenges, solutions and future research directions of privacy preservation in CPSs.
KW - Authentication
KW - Blockchain
KW - Cryptography
KW - Cyber-Physical Systems
KW - Machine learning
KW - Perturbation
KW - Privacy preservation
UR - http://www.scopus.com/inward/record.url?scp=85104574215&partnerID=8YFLogxK
U2 - 10.1109/MSN50589.2020.00121
DO - 10.1109/MSN50589.2020.00121
M3 - Conference contribution
AN - SCOPUS:85104574215
T3 - Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020
SP - 711
EP - 717
BT - Proceedings - 2020 16th International Conference on Mobility, Sensing and Networking, MSN 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 16th International Conference on Mobility, Sensing and Networking, MSN 2020
Y2 - 17 December 2020 through 19 December 2020
ER -