Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Harry Chandra Tanuwidjaja"'
Autor:
Muhamad Erza Aminanto, R. Satrio Hariomurti Wicaksono, Achmad Eriza Aminanto, Harry Chandra Tanuwidjaja, Lin Yola, Kwangjo Kim
Publikováno v:
IEEE Access, Vol 10, Pp 36791-36801 (2022)
The rise of interconnected devices through wireless networks provides two sides consequences. On one side, it helps many human tasks; on the other hand, the prone wireless medium opens the vulnerable system to be exploited by adversaries. An Intrusio
Externí odkaz:
https://doaj.org/article/1a66d052065f478280b7ce65ab620bc2
Publikováno v:
IEEE Access, Vol 8, Pp 167425-167447 (2020)
The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision-making, has become a
Externí odkaz:
https://doaj.org/article/8b24e00b7d8e4448b729b1201314f6ea
Autor:
Fabien Charmet, Harry Chandra Tanuwidjaja, Solayman Ayoubi, Pierre-François Gimenez, Yufei Han, Houda Jmila, Gregory Blanc, Takeshi Takahashi, Zonghua Zhang
Publikováno v:
Annals of Telecommunications-annales des télécommunications
Annals of Telecommunications-annales des télécommunications, 2022, 77 (11-12), pp.789-812. ⟨10.1007/s12243-022-00926-7⟩
Annals of Telecommunications-annales des télécommunications, 2022, 77 (11-12), pp.789-812. ⟨10.1007/s12243-022-00926-7⟩
With the extensive application of deep learning (DL) algorithms in recent years, e.g., for detecting Android malware or vulnerable source code, artificial intelligence (AI) and machine learning (ML) are increasingly becoming essential in the developm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fe82f9975b349597a2315f0df60294fb
https://doi.org/10.1007/s12243-022-00926-7
https://doi.org/10.1007/s12243-022-00926-7
Publikováno v:
IEEE Access. 8:167425-167447
The exponential growth of big data and deep learning has increased the data exchange traffic in society. Machine Learning as a Service, (MLaaS) which leverages deep learning techniques for predictive analytics to enhance decision-making, has become a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cd50ea65d52971caceb0fb1b717d6337
https://doi.org/10.1109/access.2020.3023084
https://doi.org/10.1109/access.2020.3023084
Autor:
Harry Chandra Tanuwidjaja, Kwangjo Kim
Publikováno v:
Privacy-Preserving Deep Learning ISBN: 9789811637636
This chapter discusses the comparison of all of privacy-preserving deep learning methods, highlighting the pros and cons of each method based on privacy parameters, used specific neural network and dataset type from the point of performance. We also
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6dad9eec18b3f12cc61ccef0a7f25598
https://doi.org/10.1007/978-981-16-3764-3_4
https://doi.org/10.1007/978-981-16-3764-3_4
Autor:
Harry Chandra Tanuwidjaja, Kwangjo Kim
Publikováno v:
Privacy-Preserving Deep Learning ISBN: 9789811637636
In this chapter, we introduce the emerging application of privacy-preserving federated learning in a coordinated way among multi-party. We suggest the function specific Privacy-Preserving Federated Learning (PPFL) to provide fairness,integrity, corre
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d38ac58ad0ee2c355eeed62a4f070cac
https://doi.org/10.1007/978-981-16-3764-3_5
https://doi.org/10.1007/978-981-16-3764-3_5
Autor:
Kwangjo Kim, Harry Chandra Tanuwidjaja
Publikováno v:
Privacy-Preserving Deep Learning ISBN: 9789811637636
This chapter categorizes the types of the adversarial model on privacy-preserving deep learning based on its behavior, define the major security goals of privacy-preserving deep learning for machine learning as a service, discuss the possible attacks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c1bdf3b703d1cf7bf38a6a8463ac3f93
https://doi.org/10.1007/978-981-16-3764-3_6
https://doi.org/10.1007/978-981-16-3764-3_6
Autor:
Harry Chandra Tanuwidjaja, Kwangjo Kim
Publikováno v:
Privacy-Preserving Deep Learning ISBN: 9789811637636
We survey the latest publications regarding X-based privacy-preserving deep learning methods based on homomorphic encryption, secure multi-party computation, differential privacy, secure enclaves and their hybrid and summarize the key features of all
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8bbe8432ff49768c9bfdae0b35cc7ed5
https://doi.org/10.1007/978-981-16-3764-3_3
https://doi.org/10.1007/978-981-16-3764-3_3
Autor:
Harry Chandra Tanuwidjaja, Kwangjo Kim
Publikováno v:
SpringerBriefs on Cyber Security Systems and Networks ISBN: 9789811637636
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fc7a3cb30df287af47d6cfc58c638a85
https://doi.org/10.1007/978-981-16-3764-3
https://doi.org/10.1007/978-981-16-3764-3
Autor:
Harry Chandra Tanuwidjaja, Kwangjo Kim
Publikováno v:
Privacy-Preserving Deep Learning ISBN: 9789811637636
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f8574f9850d58f2b792c45086a8ada9
https://doi.org/10.1007/978-981-16-3764-3_1
https://doi.org/10.1007/978-981-16-3764-3_1