Privacy Regulations Challenges on Data-centric and IoT Systems: A Case Study for Smart Vehicles

Campanile, Lelio; Iacono, Mauro; Marulli, Fiammetta; Mastroianni, Michele

doi:10.5220/0009839305070518

Cited by 14 publications

(8 citation statements)

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“…Ideally, services should use the least amount of information necessary to perform the task and/or provide the service effectively, which is a crucial requirement of the GDPR [35]. This is understood in principle, but is very difficult to quantify and communicate to the user, and new approaches are necessary for data-centric privacy in the IoV [1].…”

Section: A Privacy In the Iovmentioning

confidence: 99%

“…Method Application [23], [29] Privacy Labels Data privacy [1], [33] Blockchain methods GDPR compliance [83], [84] Virtual Trip Lines Anonymous data collection [96] Private Info. Retrieval Anonymous data retrieval [97]- [99] Differential Privacy Anonymous data retrieval…”

Section: Papermentioning

confidence: 99%

“…This was introduced with the advent of cryptocurrencies and refers to a decentralised and distributed digital record of transactions across computers, with applications in privacy protection and cryptography. In [1], the IoV is used as a case-study of the IoT, and integrating the blockchain into IoV system architecture is found to have a significant positive effect on preserving user privacy and complying with GDPR privacy regulations. A formal model is proposed in [33], for automatically verifying GDPR compliance on data processing units, and for verifying the compliance of smart devices during their design.…”

Section: Papermentioning

confidence: 99%

“…T HE Internet of Vehicles (IoV) is an emerging field which is generally viewed as an extension of the Internet of Things (IoT) [1]. The latter is a global network that connects smart devices with each other.…”

Section: Introductionmentioning

confidence: 99%

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Privacy and Trust in the Internet of Vehicles

Zavvos

Gerding

Yazdanpanah

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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The Internet of Vehicles aims to fundamentally improve transportation by connecting vehicles, drivers, passengers, and service providers together. Several new services such as parking space identification, platooning and intersection control-to name just a few-are expected to improve traffic congestion, reduce pollution, and improve the efficiency, safety and logistics of transportation. Proposed end-user services, however, make extensive use of private information with little consideration for the impact on users and third parties (those individuals whose information is indirectly involved). This article provides the first comprehensive overview of privacy and trust issues in the Internet of Vehicles at the service level. Various concerns over privacy are formalised into four basic categories: privacy of personal information, trust, consent to provide information, and multi-party privacy. To help analyse services and to facilitate future research, the main relevant end-user services are taxonomised according to voluntary and involuntary information they require and produce. Finally, this work identifies several open research problems and highlights general approaches to address them. These especially relate to measuring the tradeoff between privacy and service functionality, automated consent negotiation, trust towards the IoV and its individual services, and identifying and resolving multi-party privacy conflicts.

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Section: A Privacy In the Iovmentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

Section: Papermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Privacy and Trust in the Internet of Vehicles

Zavvos

Gerding

Yazdanpanah

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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“…However, blockchain introduces a computational overload that is not negligible for its actual application. As an example, in [13,14], a blockchain-based service infrastructure is proposed that matches European GDPR and privacy-related issues in the Internet of Vehicles (IoV) context.…”

Section: Motivationmentioning

confidence: 99%

A Privacy-Oriented Approach for Depression Signs Detection Based on Speech Analysis

et al. 2021

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Currently, AI-based assistive technologies, particularly those involving sensitive data, such as systems for detecting mental illness and emotional disorders, are full of confidentiality, integrity, and security compromises. In the aforesaid context, this work proposes an algorithm for detecting depressive states based on only three never utilized speech markers. This reduced number of markers offers a valuable protection of personal (sensitive) data by not allowing for the retrieval of the speaker’s identity. The proposed speech markers are derived from the analysis of pitch variations measured in speech data obtained through a tale reading task performed by typical and depressed subjects. A sample of 22 subjects (11 depressed and 11 healthy, according to both psychiatric diagnosis and BDI classification) were involved. The reading wave files were listened to and split into a sequence of intervals, each lasting two seconds. For each subject’s reading and each reading interval, the average pitch, the pitch variation (T), the average pitch variation (A), and the inversion percentage (also called the oscillation percentage O) were automatically computed. The values of the triplet (Ti, Ai, Oi) for the i-th subject provide, all together, a 100% correct discrimination between the speech produced by typical and depressed individuals, while requiring a very low computational cost and offering a valuable protection of personal data.

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