An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors
Abstract
:1. Introduction
- A comprehensive analysis of the personal data collected using mobile background sensors and the related machine-learning- and deep-learning-based automated methods that focus on sociological and demographic aspects.
- A presentation of the generally considered applications and real-world use case scenarios related to the use of mobile devices.
- An overview of the relevant sensors and the related raw data usually available in mobile computing environments and mobile devices. This particularly analyzes the background sensors, as they are usually perceived as harmless by the average end user.
- A presentation of the metrics introduced in the relevant scientific and technical literature.
2. Research Methodology
2.1. Research Questions
- What are the sensors and the raw data commonly available on modern mobile devices, paying special attention to background sensors, which are often considered harmless by the end users?
- What are the typical related real-world use case scenarios?
- What are the relevant practical purposes of the data that are collected using mobile sensors?
- What are the specific features and logical structure of the data, which pertain to the various analyzed real-world use-case scenarios?
- What are the most frequently used data privacy and anonymization techniques?
- What are the metrics that quantify the level of data anonymization processes?
2.2. Research Process
2.3. Exclusion and Inclusion Criteria
- Step 1. Abstract-based filtering: irrelevant scientific contributions are ignored based on the information that is extracted from the abstract and also considering the keywords. Thus, papers that meet at least 50% of the relevance threshold are considered further.
- Step 2. Full text-based filtering: papers that address only a small part of the scientific scope, which is defined by the abstract and the keywords, are ignored.
- Step 3. Quality analysis-based filtering: the remaining papers are further filtered out if any of the following conditions are not satisfied: <The paper proposes a comprehensive solution regarding the usage of data-driven soft sensors.> AND <The paper thoroughly describes the technical implementation of the proposed solution.> AND <The paper reviews related similar scientific contributions.> AND <The paper discusses and analyzes the obtained results.>
3. Data Acquisition through Mobile Devices and Sensors
3.1. Remarks Concerning Full Privacy-Preserving Data Computation
- The collection of personal data is conducted using mobile client devices.
- The data is transferred to central data processing components.
- The data are properly and securely stored, and privacy-preserving data is processed.
- The system should be specified considering a flexible and decoupled system architecture which would allow for an efficient extension and re-structuring of the system in the future.
- The legal and formal requirements that are formalized by American and European regulations are also considered.
- The efficient integration of the system in the target software frameworks considers the specifics of the respective use cases, as well as all the technical and legal requirements.
3.2. Analytical Remarks Concerning Similar Contributions
4. General Mobile Collection of Sensitive Data
5. Real-World Sensors Use Case Scenarios
5.1. User Authentication Systems
5.2. Fitness and Healthcare Systems and Services
5.3. Services Based on Location Data
5.4. Remarks Concerning Other Relevant Use Cases
6. Proper Management of Sensitive Private Data
6.1. Demographic Data
6.1.1. Sensors That Detect Movement
6.1.2. Touchscreen Data
6.1.3. Sensor Data Related to Mobile Applications, Location, and Network
6.2. Remarks Concerning the Study of Human Behaviour
6.2.1. Motion Sensors
6.2.2. Sensor Data Related to Mobile Applications, Location, and Network
6.3. Remarks Regarding Body Features and Health Parameters
6.3.1. Motion Sensors
6.3.2. Remarks Concerning the Touchscreen
6.3.3. Sensors Data Related to Mobile Applications, Location, and Network
6.4. The Detection of Psychological Mood and Emotions
6.4.1. Motion Sensors
6.4.2. Touchscreen Data
6.4.3. Sensors Data Related to Mobile Applications, Location, and Network
6.5. User Tracking through Location Data
6.5.1. Motion Sensors
6.5.2. Sensor Data Related to Mobile Applications, Location, and Network
6.6. Logging Keystroke Data and Text Inference Using Motion Sensors
7. Metrics Related to the Privacy of Personal Sensitive Data
7.1. General Considerations
7.1.1. Metrics That Relate to Data Anonymity
7.1.2. Differential Metrics
7.1.3. Metrics That Consider Entropy
7.1.4. Metrics Based on the Probability of Success
7.1.5. Metrics Based on the Concept of Error
7.1.6. Metrics Based on the Concept of Accuracy
7.1.7. Metrics Based on the Concept of Time
8. Analytical Discussion Concerning Relevant Research Aspects and Gaps
Further Remarks
9. Conclusions and Open Questions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Scientific Literature Source | Source Type | Public URL |
---|---|---|
Science Direct-Elsevier | Digital library | Science Direct (http://www.sciencedirect.com/, accessed on 23 December 2022) |
Scopus | Search engine | Scopus (http://www.scopus.com/, accessed on 23 December 2022) |
IEEE Xplore | Digital library | IEEE Xplore (http://ieeexplore.ieee.org/Xplore/home.jsp, accessed on 23 December 2022) |
ACM Digital library | Digital library | ACM Digital library (http://dl.acm.org/dl.cfm, accessed on 23 December 2022) |
Web of science | Search engine | Web of science (https://www.webofknowledge.com/, accessed on 23 December 2022) |
Wiley online library | Digital library | Wiley online library (https://onlinelibrary.wiley.com/, accessed on 23 December 2022) |
Google Scholar | Search engine | Google Scholar (https://scholar.google.ro/, accessed on 23 December 2022) |
Sensors | Digital library | MDPI Sensors Journal https://www.mdpi.com/journal/sensors, accessed on 23 December 2022) |
Springer | Digital library | Springer digital library (https://www.springer.com/, accessed on 23 December 2022) |
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Edinburgh library database | Digital library | Edinburgh library database (https://my.napier.ac.uk/Library/, accessed on 23 December 2022) |
Inclusion Criteria |
---|
Papers should be indexed by at least one of the presented scientific paper sources. |
Contributions are reported in the period 2010–2022, while relevant older historic papers are also considered. |
Papers should fulfill at least one of the search terms, as designated by the title, abstract, and keywords of this survey paper. |
Contributions should be published in indexed journals, conference proceedings, or mainstream technical journals. |
Surveyed papers should clearly address and answer defined research questions. |
A search that considers title, abstract, and full text is sufficient. |
Exclusion Criteria |
---|
Papers that are not written in English. |
Duplicated papers, which are found using more than one of the specified scientific literature sources. |
Papers with full texts that are impossible to access. |
Papers that are only marginally relevant to the usage of data-driven soft sensors, related deep learning models, and data anonymization techniques. |
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Bocu, R.; Bocu, D.; Iavich, M. An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors. Sensors 2023, 23, 294. https://doi.org/10.3390/s23010294
Bocu R, Bocu D, Iavich M. An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors. Sensors. 2023; 23(1):294. https://doi.org/10.3390/s23010294
Chicago/Turabian StyleBocu, Razvan, Dorin Bocu, and Maksim Iavich. 2023. "An Extended Review Concerning the Relevance of Deep Learning and Privacy Techniques for Data-Driven Soft Sensors" Sensors 23, no. 1: 294. https://doi.org/10.3390/s23010294