W@rk: Attendance application framework using blockchain technology

Kaha, Putra Roskhairul Fitri; Rahayu, Syarifah Bahiyah; Azahari, Afiqah M.; Halip, Mohd Hazali Mohamed; Venkatesan, K.
DATASET 2023, International Conference on Data Science and Emerging Technologies, 4-5 December 2023, Malaysia, (Online event) / Also published in Lecture Notes on Data Engineering and Communications Technologies, Vol.191, 2024

Post-COVID has inadvertently caused a widespread shift toward remote working for many employees across the globe. This new normal has necessitated a focus on employee’s well-being, making it a top priority for companies. However, many businesses are concerned about the potential for abuse or remote working arrangements and the risk of data security breaches due to various security concerns. As a result, there is a need to develop a new attendance application that can facilitate a shift from a work-from-office setup to a work-from-anywhere arrangement. This paper introduces a contactless attendance, W@RK, embedded with a face recognition feature and location-based for recording employee attendance using blockchain technology. The aim is to monitor employee attendance and empower employees to work from anywhere. The objective is to develop an alternative contactless attendance application. Agile software development methodology is adopted to develop this attendance application. A facial recognition feature is used to scan the face of employees to authenticate themselves through verification services. While the location is detected using geo-fencing, the blockchain technology records and verifies the attendance of the employee. The facial data is recorded on a blockchain, along with a timestamp and other relevant information such as location. The findings show the proposed framework has a promising capability to record and verify attendance securely and efficiently. As the use of blockchain technology continues to grow, it is likely more organizations will adopt facial attendance systems to track the attendance of employees, students, and other individuals at events and meetings.


DOI
Type:
Conference/Book
Date:
2023-12-04
Department:
Digital Security
Eurecom Ref:
8033
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in DATASET 2023, International Conference on Data Science and Emerging Technologies, 4-5 December 2023, Malaysia, (Online event) / Also published in Lecture Notes on Data Engineering and Communications Technologies, Vol.191, 2024 and is available at : https://doi.org/10.1007/978-981-97-0293-0_34
See also:

PERMALINK : https://www.eurecom.fr/publication/8033