The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Privacy Risk Assessment of Smart Home System Based on a STPA–FMEA Method
Open accessAbstract
Although the smart home industry is rapidly emerging, it faces the risk of privacy security that cannot be neglected. As this industry now has a complex combination system involving multiple subjects, it is difficult for the traditional risk assessment method to meet these new security requirements. In this study, a privacy risk assessment method based on the combination of system theoretic process analysis–failure mode and effect analysis (STPA–FMEA) is proposed for a smart home system, considering the interaction and control of ‘user-environment-smart home product’. A total of 35 privacy risk scenarios of ‘component-threat-failure-model-incident’ combinations are identified. The risk priority numbers (RPN) was used to quantitatively assess the level of risk for each risk scenario and the role of user and environmental factors in influencing the risk. According to the results, the privacy management ability of users and the security state of the environment have significant effects on the quantified values of the privacy risks of smart home systems. The STPA–FMEA method can identify the privacy risk scenarios of a smart home system and the insecurity constraints in the hierarchical control structure of the system in a relatively comprehensive manner. Additionally, the proposed risk control measures based on the STPA–FMEA analysis can effectively reduce the privacy risk of the smart home system. The risk assessment method proposed in this study can be widely applied to the field of risk research of complex systems, and this study can contribute to the improvement of privacy security of smart home systems.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Survey on the Internet of Things (IoT) Forensics: Challenges, Approaches, and Open Issues
Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2962586 · Cited by 852
Securing Fog Computing for Internet of Things Applications: Challenges and Solutions
Ni, J., Zhang, K., Lin, X., & Shen, X. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2762345 · Cited by 659 · cited 1×
“[16] introduced a fog computing program to enhance IoT functionality, and the study found that fog computing has new requirements for system privacy and security.”
Background
Research works printed but not linked: 36
- IDC (2022, October 27). Worldwide Quarterly Smart Home Device Tracker. Available online: https://www.idc.com/getdoc.jsp?containerId=IDC_P37480.
- Ventikos, 2020, A systems-based application for autonomous vessels safety: Hazard identification as a function of increasing autonomy levels, Saf. Sci., 131, 104919, 10.1016/j.ssci.2020.104919 · DOI
- Li, 2021, Motivations, barriers and risks of smart home adoption: From systematic literature review to conceptual framework, Energy Res. Soc. Sci., 80, 102211, 10.1016/j.erss.2021.102211 · DOI
- Bugeja, J., Jacobsson, A., and Davidsson, P. (2021). PRASH: A Framework for Privacy Risk Analysis of Smart Homes. Sensors, 21. · DOI
- Yang, 2015, Validating FMEA output against incident learning data: A study in stereotactic body radiation therapy, Med. Phys., 42, 2777, 10.1118/1.4919440 · DOI
- Maisano, 2020, dP-FMEA: An innovative Failure Mode and Effects Analysis for distributed manufacturing processes, Qual. Eng., 32, 267, 10.1080/08982112.2020.1729991 · DOI
- Yamaguchi, 2019, A system safety approach for tomographic treatment, Saf. Sci., 118, 772, 10.1016/j.ssci.2019.05.041 · DOI
- Leveson, 2004, A new accident model for engineering safer systems, Saf. Sci., 42, 237, 10.1016/S0925-7535(03)00047-X · DOI
- Bensaci, 2020, STPA and Bowtie risk analysis study for centralized and hierarchical control architectures comparison, Alex. Eng. J., 59, 3799, 10.1016/j.aej.2020.06.036 · DOI
- Shapiro, S.S. (2016, January 22–26). Privacy Risk Analysis Based on System Control Structures Adapting System-Theoretic Process Analysis for Privacy Engineering. Proceedings of the 37th IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA. · DOI
- Liu, Z.S., Zhang, A.S., and Wang, W.S. (2020). A Framework for an Indoor Safety Management System Based on Digital Twin. Sensors, 20. · DOI
- Kliestik, 2022, Digital Twin Simulation and Modeling Tools, Computer Vision Algorithms, and Urban Sensing Technologies in Immersive 3D Environments, Geopolit. Hist. Int. Relat., 14, 9, 10.22381/GHIR14120221 · DOI
- Rowland, 2022, The Geopolitics of Smart City Digital Twins: Urban Sensing and Immersive Virtual Technologies, Spatio-Temporal Fusion Algorithms, and Visualization Modeling Tools, Geopolit. Hist. Int. Relat., 14, 56, 10.22381/GHIR14220224 · DOI
- Zvarikova, 2022, Multisensor Fusion and Dynamic Routing Technologies, Virtual Navigation and Simulation Modeling Tools, and Image Processing Computational and Visual Cognitive Algorithms across Web3-Powered Metaverse Worlds, Anal. Metaphys., 21, 125, 10.22381/am2120228 · DOI
- Wilson, 2017, Benefits and risks of smart home technologies, Energy Policy, 103, 72, 10.1016/j.enpol.2016.12.047 · DOI · Paper page
- Shaw, 2019, The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value, Int. J. Inform. Manag., 45, 44, 10.1016/j.ijinfomgt.2018.10.024 · DOI
- Meneghello, 2019, IoT: Internet of Threats? A Survey of Practical Security Vulnerabilities in Real IoT Devices, IEEE Internet Things J., 6, 8182, 10.1109/JIOT.2019.2935189 · DOI
- Kirkham, 2014, Risk driven Smart Home resource management using cloud services, Future Gener. Comput. Syst.-Int. J. Esci., 38, 13, 10.1016/j.future.2013.08.006 · DOI
- Jacobsson, 2016, A risk analysis of a smart home automation system, Future Gener. Comput. Syst.-Int. J. Esci., 56, 719, 10.1016/j.future.2015.09.003 · DOI
- Nurse, J.R., Atamli, A., and Martin, A. (2016). International Conference on Human Aspects of Information Security, Privacy, and Trust, Springer.
- Psychoula, I., Chen, L.M., and Chen, F. (2017, January 12–15). Privacy Modelling and Management for Assisted Living within Smart Homes. Proceedings of the 19th Annual IEEE International Conference on E-Health Networking, Applications and Services (Healthcom), Dalian, China. · DOI
- Krichen, M., and Alroobaea, R. (2019, January 4–5). A New Model-based Framework for Testing Security of IoT Systems in Smart Cities using Attack Trees and Price Timed Automata. Proceedings of the 14th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE), Heraklion, Greece, SCITEPRESS—Science and Technology Publications, Heraklion, Crete, Greece. · DOI
- Sturgess, J., Nurse, J.R.C., and Zhao, J. (2018, January 28–29). A capability-oriented approach to assessing privacy risk in smart home ecosystems. Proceedings of the Living in the Internet of Things: Cybersecurity of the IoT–2018, London, UK. · DOI
- Park, M., Oh, H., and Lee, K. (2019). Security Risk Measurement for Information Leakage in IoT-Based Smart Homes from a Situational Awareness Perspective. Sensors, 19. · DOI
- Yang, 2020, Security and Privacy of Smart Home Systems Based on the Internet of Things and Stereo Matching Algorithms, IEEE Internet Things, 7, 2521, 10.1109/JIOT.2019.2946214 · DOI
- Heartfield, 2021, Self-Configurable Cyber-Physical Intrusion Detection for Smart Homes Using Reinforcement Learning, IEEE Trans. Inf. Forensics Secur., 16, 1720, 10.1109/TIFS.2020.3042049 · DOI
- Tan, 2017, Privacy-Cost Trade-Offs in Demand-Side Management with Storage, IEEE Trans. Inf. Forensics Secur., 12, 1458, 10.1109/TIFS.2017.2656469 · DOI
- Miandashti, 2020, An Empirical Approach to Modeling User-System Interaction Conflicts in Smart Homes, IEEE Trans. Hum.-Mach. Syst., 50, 573, 10.1109/THMS.2020.3017784 · DOI
- Kulik, 2022, A Survey of Practical Formal Methods for Security, Form. Asp. Comput., 34, 1, 10.1145/3522582 · DOI
- Ortiz, 2018, Information privacy, consumer alienation, and lurking behavior in social networking sites, Comput. Hum. Behav., 80, 143, 10.1016/j.chb.2017.11.005 · DOI
- Wang, 2019, Effects of Brand Awareness and Social Norms on User-Perceived Cyber Privacy Risk, Int. J. Electron. Comm., 23, 272, 10.1080/10864415.2018.1564553 · DOI
- Khan, 2021, Systematic Analysis of Safety and Security Risks in Smart Homes, Cmc.-Comput. Mater. Con., 68, 1409
- Allison, 2017, Systems Theoretic Accident Model and Process (STAMP) safety modelling applied to an aircraft rapid decompression event, Saf. Sci., 98, 159, 10.1016/j.ssci.2017.06.011 · DOI
- Pereira, 2019, A STAMP-based ontology approach to support safety and security analyses, J. Inf. Secur. Appl., 47, 302
- Zhang, 2022, Risk assessment based on a STPA-FMEA method: A case study of a sweeping robot, Risk Anal., 43, 13927
- Duezguen, R., Mayer, P., Berens, B., Beckmann, C., Aldag, L., Mossano, M., Volkamer, M., and Strufe, T. (2021, January 20–22). How to Increase Smart Home Security and Privacy Risk Perception. Proceedings of the 20th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (IEEE TrustCom), Shenyang, China. · DOI
Metadata
- Authors
- Wang, Y., Zhang, R., Zhang, X., & Zhang, Y.
- Affiliations
- China University of Mining and Technology - Beijing (CN)
- Corresponding author
- Zhang, Rui
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2023-05-11
- Volume
- 23
- Issue
- 10
- Pages
- 4664
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC10220840
- OpenAlex
- W4382141681
- Cited by
- 28
- References
- 2
- Citations by year
- 2026: 5 · 2025: 9 · 2024: 12 · 2023: 2
- FWCI
- 2.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Research on important technical standards for quality and safety risk control of typical intelligent products (2022YFF0607100)
- Topics
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Physical Sciences › Engineering › Safety, Risk, Reliability and Quality › Risk and Safety Analysis
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Keywords
- smart home systems, privacy risk assessment, STPA, failure mode and effects analysis, privacy security, risk scenarios, risk priority number, environmental security, risk control, hierarchical control architecture
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Privacy Risk Assessment of Smart Home System Based on a STPA–FMEA Method. https://app.cochat.ai/papers/10.3390/s23104664
- The paper
- Wang, Y., Zhang, R., Zhang, X., & Zhang, Y. (2023). Privacy Risk Assessment of Smart Home System Based on a STPA–FMEA Method. Sensors. https://doi.org/10.3390/s23104664