Assistive Multimodal Robotic System (AMRSys): Security and Privacy Issues, Challenges, and Possible Solutions
Open accessAbstract
Assistive robotic systems could be a suitable solution to support a variety of health and care services, help independent living, and even simulate affection, to reduce loneliness. However, adoption is limited by several issues, as well as user concerns about ethics, data security, and privacy. Other than the common threats related to internet connectivity, personal robotic systems have advanced interaction possibilities, such as audio, video, touch, and gestures, which could be exploited to gain access to private data that are stored in the robot. Therefore, novel, safer methods of interaction should be designed to safeguard users’ privacy. To solicit further research on secure and private multimodal interaction, this article presents a thorough study of the state-of-the-art literature on data security and user privacy in interactive social robotic systems for health and care. In our study, we focus on social robotics to assist older people, which is a global challenge that is receiving a great deal of attention from the robotics and social care communities. This application will have a significant positive impact on the economy and society, but poses various security and privacy issues. This article analyses the key vulnerable areas where data leakage could occur during a multimodal interaction with a personal assistive robotic system. Thus, blockchain with a resource-aware framework, along with a continuous multifactor authentication mechanism, are envisaged as a potential solution for making such systems secure by design; therefore, increasing trust, acceptability, and adoption. Among the key cybersecurity research challenges, it is crucial to create an intelligent mechanism that autonomously determines the right trade-off between continuous user prompts and system usability, according to data types and personal preferences.
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RNNs excel in sequence modeling, making them particularly effective for tasks involving language and temporal data, because their architecture is designed to process sequences of data such as text, enabling them to capture context and order.
“RNNs excel in the realm of sequence modeling, making them particularly effective for tasks involving language and temporal data, as their architecture is specifically designed to process sequences of data, such as text, enabling them to capture the context and order of the input effectively [150], [151], [152], [153], [154].”
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- Authors
- Marchang, J., & Di Nuovo, A.
- Affiliations
- Sheffield Hallam University (GB)
- Computing Department & Advanced Wellbeing Research Centre (AWRC), Sheffield Hallam University, Sheffield S1 1WB, UK
- Corresponding author
- Marchang, Jims
- Venue
- Applied Sciences
- Publisher
- MDPI AG
- Published
- 2022-02-19
- Volume
- 12
- Issue
- 4
- Pages
- 2174
- ISSN
- 2076-3417 (ISSN-L)
- OpenAlex
- W4213107980
- Cited by
- 33
- References
- 64
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 9 · 2023: 7 · 2022: 1
- FWCI
- 6.14
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Applied Sciences (publishedVersion, cc-by)
- SHURA (Sheffield Hallam University Research Archive) (Sheffield Hallam University) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Engineering and Physical Sciences Research Council (EP/W000741/1)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Engineering › Control and Systems Engineering › Robotics and Automated Systems
- Keywords
- assistive robotics, social robots, multimodal interaction, data privacy, data security, older adults, health and care, blockchain, multi-factor authentication, security by design, data leakage, user trust
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 ›
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