Strategi Teknologi dan Kebijakan untuk Menjamin Privasi Data Pengguna dalam Perpustakaan Digital Era Modern
-
Pratama Dahlian Persadha
*
Sekolah Tinggi Inteljen Negara, Bogor,
Indonesia
- Loso Judijanto IPOSS Jakarta, Jakarta, Indonesia
- Melly Susanti Universitas Muhammadiyah Bengkulu, Bengkulu, Indonesia
- Heru Kreshna Reza Universitas Esa Unggul, Jakarta, Indonesia (*) Corresponding Author
Abstract
Security is becoming a very important element in the digital age, especially in the management and protection of information. With the increasing volume of information being processed, the need for adequate knowledge management and security provision is becoming more pressing. This research highlights the importance of cybersecurity in the context of digital libraries that must comply with certain technological standards and regulations to protect user data and ensure privacy when accessing electronic resources. Libraries face various challenges in protecting personal data on their electronic resources. This research explores topics such as user privacy, data encryption, access management, and compliance with privacy laws. By addressing these issues thoroughly, libraries can ensure the protection of user privacy while optimizing the benefits of digital resources in today's information environment. The October 2023 cyberattack by the hacker group Rhysida on the British Library's information systems underscores the importance of cybersecurity and data privacy for digital libraries. This research aims to provide insights and solutions to these challenges, so that digital libraries can operate securely and efficiently.
Downloads
References
Aldaej, A. (2021). Notice of Retraction: Enhancing Cyber Security in Modern Internet of things (IoT) Using Intrusion Prevention Algorithm for IoT (IPAI). In IEEE Access (p. 1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ACCESS.2019.2893445
Alwahedi, F., Aldhaheri, A., Ferrag, M. A., Battah, A., & Tihanyi, N. (2024). Machine learning techniques for IoT security: Current research and future vision with generative AI and large language models. In Internet of Things and Cyber-Physical Systems (Vol. 4, pp. 167–185). Elsevier BV. https://doi.org/10.1016/j.iotcps.2023.12.003
Biswas, S., & Palamidessi, C. (2024). PRIVIC: A privacy-preserving method for incremental collection of location data. In Proceedings on Privacy Enhancing Technologies (Vol. 2024, Issue 1, pp. 582–596). Privacy Enhancing Technologies Symposium Advisory Board. https://doi.org/10.56553/popets-2024-0033
Brighente, A., Conti, M., Renzone, G. Di, Peruzzi, G., & Pozzebon, A. (2024). Security and Privacy of Smart Waste Management Systems: A Cyber-Physical System Perspective. In IEEE Internet of Things Journal (Vol. 11, Issue 5, pp. 7309–7324). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/JIOT.2023.3322532
Chen, H., & Babar, M. A. (2024). Security for Machine Learning-based Software Systems: A Survey of Threats, Practices, and Challenges. In ACM Computing Surveys (Vol. 56, Issue 6, pp. 1–38). Association for Computing Machinery (ACM). https://doi.org/10.1145/3638531
Davies, J. (2023). Enhanced scalability and privacy for blockchain data using Merklized transactions. In Frontiers in Blockchain (Vol. 6). Frontiers Media SA. https://doi.org/10.3389/fbloc.2023.1222614
Demirer, M., Jiménez Hernández, D., Li, D., & Peng, S. (2024). Data, Privacy Laws and Firm Production: Evidence from the GDPR. In SSRN Electronic Journal. Elsevier BV. https://doi.org/10.2139/ssrn.4718871
Du, Z., Li, Y., Fu, Y., & Zheng, X. (2024). Blockchain-based access control architecture for multi-domain environments. In Pervasive and Mobile Computing (Vol. 98, p. 101878). Elsevier BV. https://doi.org/10.1016/j.pmcj.2024.101878
Eghmazi, A., Ataei, M., Landry, R. J., & Chevrette, G. (2024). Enhancing IoT Data Security: Using the Blockchain to Boost Data Integrity and Privacy. In Internet of Things (Vol. 5, Issue 1, pp. 20–34). MDPI AG. https://doi.org/10.3390/iot5010002
Filani, J. (2024). Data Privacy in the Digital Age: Analyzing the impact of Technology of U.S Privacy Regulations. In SSRN Electronic Journal. Elsevier BV. https://doi.org/10.2139/ssrn.4762809
Hanisch, S., Todt, J., Patino, J., Evans, N., & Strufe, T. (2024). A False Sense of Privacy: Towards a Reliable Evaluation Methodology for the Anonymization of Biometric Data. In Proceedings on Privacy Enhancing Technologies (Vol. 2024, Issue 1, pp. 116–132). Privacy Enhancing Technologies Symposium Advisory Board. https://doi.org/10.56553/popets-2024-0008
Hashem, T. N. (2024). Examining marketing cyber-security in the digital age: Evidence from marketing platforms. In International Journal of Data and Network Science (Vol. 8, Issue 2, pp. 1141–1150). Growing Science. https://doi.org/10.5267/j.ijdns.2023.11.020
Khan, I. A., Razzak, I., Pi, D., Khan, N., Hussain, Y., Li, B., & Kousar, T. (2024). Fed-Inforce-Fusion: A federated reinforcement-based fusion model for security and privacy protection of IoMT networks against cyber-attacks. In Information Fusion (Vol. 101, p. 102002). Elsevier BV. https://doi.org/10.1016/j.inffus.2023.102002
Kumari, P., Natesan, D. G., & Kumar, M. (2024). Exploring Frontiers in Big Data: Privacy-Preserving Exchange and Data Lake Innovations. In International Journal of Research Publication and Reviews (Vol. 5, Issue 3, pp. 862–865). Genesis Global Publication. https://doi.org/10.55248/gengpi.5.0324.0637
Kutschera, S., Slany, W., Ratschiller, P., Gursch, S., Deininger, P., & Dagenborg, H. (2024). Incidental Data: A Survey towards Awareness on Privacy-Compromising Data Incidentally Shared on Social Media. In Journal of Cybersecurity and Privacy (Vol. 4, Issue 1, pp. 105–125). MDPI AG. https://doi.org/10.3390/jcp4010006
Ling, J., Zheng, J., & Chen, J. (2024). Efficient federated learning privacy preservation method with heterogeneous differential privacy. In Computers and Security (Vol. 139, p. 103715). Elsevier BV. https://doi.org/10.1016/j.cose.2024.103715
Liu, J., Tang, Y., Zhao, H., Wang, X., Li, F., & Zhang, J. (2024). CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach. In ACM Transactions on Sensor Networks (Vol. 20, Issue 2, pp. 1–27). Association for Computing Machinery (ACM). https://doi.org/10.1145/3585520
Liu, Z., Guo, J., Yang, W., Fan, J., Lam, K. Y., & Zhao, J. (2022). Privacy-Preserving Aggregation in Federated Learning: A Survey. In IEEE Transactions on Big Data (pp. 1–20). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/TBDATA.2022.3190835
Manalo, M. L. B., & Gallardo, R. D. (2024). Cyber Security Awareness and Educational Outcomes of Grade 4 Learners. In International Journal of Innovative Science and Research Technology (IJISRT) (pp. 1390–1422). International Journal of Innovative Science and Research Technology. https://doi.org/10.38124/ijisrt/ijisrt24apr1261
Masood, I., Daud, A., Wang, Y., Banjar, A., & Alharbey, R. (2024). A blockchain-based system for patient data privacy and security. In Multimedia Tools and Applications. Springer Science and Business Media LLC. https://doi.org/10.1007/s11042-023-17941-y
Mlika, F., Karoui, W., & Romdhane, L. Ben. (2024). Blockchain solutions for trustworthy decentralization in social networks. In Computer Networks (Vol. 244, p. 110336). Elsevier BV. https://doi.org/10.1016/j.comnet.2024.110336
Munilla Garrido, G., Nair, V., & Song, D. (2024). SoK: Data Privacy in Virtual Reality. In Proceedings on Privacy Enhancing Technologies (Vol. 2024, Issue 1, pp. 21–40). Privacy Enhancing Technologies Symposium Advisory Board. https://doi.org/10.56553/popets-2024-0003
Patil, R. A., & Patil, P. D. (2024). Efficient approximation and privacy preservation algorithms for real time online evolving data streams. In World Wide Web (Vol. 27, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1007/s11280-024-01244-9
Sedlak, B., Murturi, I., Donta, P. K., & Dustdar, S. (2023). A Privacy Enforcing Framework for Data Streams on the Edge. In IEEE Transactions on Emerging Topics in Computing (pp. 1–12). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/TETC.2023.3315131
Seeman, J., & Susser, D. (2024). Between Privacy and Utility: On Differential Privacy in Theory and Practice. In ACM Journal on Responsible Computing (Vol. 1, Issue 1, pp. 1–18). Association for Computing Machinery (ACM). https://doi.org/10.1145/3626494
Sharma, S., & Dwivedi, R. (2024). A survey on blockchain deployment for biometric systems. In IET Blockchain. Institution of Engineering and Technology (IET). https://doi.org/10.1049/blc2.12063
Sharma, S., & Nebhnani, M. (2024). Securing the Digital Frontier: Data Science Applications in Cyber security and Anomaly Detection. In International Journal of Food and Nutritional Sciences (Vol. 09, Issue 03). Institute for Advanced Studies. https://doi.org/10.48047/ijfans/09/03/33
Singh, R. K. (2024). Developing a big data analytics platform using Apache Hadoop Ecosystem for delivering big data services in libraries. In Digital Library Perspectives (Vol. 40, Issue 2, pp. 160–186). Emerald. https://doi.org/10.1108/DLP-10-2022-0079
Song, F., Li, L., Yikun, Li, Ma, Y., Wang, L., & Zhang, H. (2021). Smart Collaborative Contract for Endogenous Access Control in Massive Machine Communications. In IEEE Internet of Things Journal (p. 1). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/JIOT.2021.3134366
Subramani, J., Maria, A., Rajasekaran, A. S., & Lloret, J. (2024). Physically secure and privacy‐preserving blockchain enabled authentication scheme for internet of drones. In Security and Privacy (Vol. 7, Issue 3). Wiley. https://doi.org/10.1002/spy2.364
Tosi, D., Kokaj, R., & Roccetti, M. (2024). 15 years of Big Data: a systematic literature review. In Journal of Big Data (Vol. 11, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1186/s40537-024-00914-9
Wen, X., Chen, Y., Zhang, W., Jiang, Z. L., & Fang, J. (2024). Quantum protection scheme for privacy data based on trusted center. In Optics and Laser Technology (Vol. 169, p. 110130). Elsevier BV. https://doi.org/10.1016/j.optlastec.2023.110130
Wu, C. (2024). Data privacy: From transparency to fairness. In Technology in Society (Vol. 76, p. 102457). Elsevier BV. https://doi.org/10.1016/j.techsoc.2024.102457
Zhang, K., Chen, K., Li, Z., Chen, J., & Zheng, Y. (2023). Privacy-Preserving Data-Enabled Predictive Leading Cruise Control in Mixed Traffic. In IEEE Transactions on Intelligent Transportation Systems (pp. 1–16). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/TITS.2023.3329484
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Strategi Teknologi dan Kebijakan untuk Menjamin Privasi Data Pengguna dalam Perpustakaan Digital Era Modern
Pages: 416-423
Copyright (c) 2024 Pratama Dahlian Persadha, Loso Judijanto, Melly Susanti, Heru Kreshna Reza
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
