A Review of Deep Learning Security and Privacy Defensive Techniques

Joint Authors

Tayyaba, Shahzadi
Tariq, Muhammad Imran
Memon, Nisar Ahmed
Ahmed, Shakeel
Mushtaq, Muhammad Tahir
Mian, Natash Ali
Imran, Muhammad
Ashraf, Muhammad W.

Source

Mobile Information Systems

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-18, 18 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-04-07

Country of Publication

Egypt

No. of Pages

18

Main Subjects

Telecommunications Engineering

Abstract EN

In recent past years, Deep Learning presented an excellent performance in different areas like image recognition, pattern matching, and even in cybersecurity.

The Deep Learning has numerous advantages including fast solving complex problems, huge automation, maximum application of unstructured data, ability to give high quality of results, reduction of high costs, no need for data labeling, and identification of complex interactions, but it also has limitations like opaqueness, computationally intensive, need for abundant data, and more complex algorithms.

In our daily life, we used many applications that use Deep Learning models to make decisions based on predictions, and if Deep Learning models became the cause of misprediction due to internal/external malicious effects, it may create difficulties in our real life.

Furthermore, the Deep Learning training models often have sensitive information of the users and those models should not be vulnerable and expose security and privacy.

The algorithms of Deep Learning and machine learning are still vulnerable to different types of security threats and risks.

Therefore, it is necessary to call the attention of the industry in respect of security threats and related countermeasures techniques for Deep Learning, which motivated the authors to perform a comprehensive survey of Deep Learning security and privacy security challenges and countermeasures in this paper.

We also discussed the open challenges and current issues.

American Psychological Association (APA)

Tariq, Muhammad Imran& Memon, Nisar Ahmed& Ahmed, Shakeel& Tayyaba, Shahzadi& Mushtaq, Muhammad Tahir& Mian, Natash Ali…[et al.]. 2020. A Review of Deep Learning Security and Privacy Defensive Techniques. Mobile Information Systems،Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1192429

Modern Language Association (MLA)

Tayyaba, Shahzadi…[et al.]. A Review of Deep Learning Security and Privacy Defensive Techniques. Mobile Information Systems No. 2020 (2020), pp.1-18.
https://search.emarefa.net/detail/BIM-1192429

American Medical Association (AMA)

Tariq, Muhammad Imran& Memon, Nisar Ahmed& Ahmed, Shakeel& Tayyaba, Shahzadi& Mushtaq, Muhammad Tahir& Mian, Natash Ali…[et al.]. A Review of Deep Learning Security and Privacy Defensive Techniques. Mobile Information Systems. 2020. Vol. 2020, no. 2020, pp.1-18.
https://search.emarefa.net/detail/BIM-1192429

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1192429