Deep Models Under the GAN

Hitaj, Briland; Ateniese, Giuseppe; Pérez-Cruz, Fernando

doi:10.1145/3133956.3134012

Cited by 901 publications

(165 citation statements)

References 30 publications

Supporting

Mentioning

108

Contrasting

Unclassified

Order By: Relevance

“…Given the large volume and diversity of training data needed for ML research, federated learning approaches, which do not require direct data sharing, have strong potential but remain difficult to implement due to privacy preservation challenges 83 . Furthermore, the “black box” nature of many algorithms renders interpretation and benchmarking performance difficult.…”

Section: Discussionmentioning

confidence: 99%

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

et al. 2020

View full text Add to dashboard Cite

Objectives: Conduct a survey of the literature for advancements in cancer informatics over the last three years in three specific areas where there has been unprecedented growth: 1) digital health; 2) machine learning; and 3) precision oncology. We also highlight the ethical implications and future opportunities within each area. Methods: A search was conducted over a three-year period in two electronic databases (PubMed, Google Scholar) to identify peer-reviewed articles and conference proceedings. Search terms included variations of the following: neoplasms[MeSH], informatics[MeSH], cancer, oncology, clinical cancer informatics, medical cancer informatics. The search returned too many articles for practical review (23,994 from PubMed and 23,100 from Google Scholar). Thus, we conducted searches of key PubMed-indexed informatics journals and proceedings. We further limited our search to manuscripts that demonstrated a clear focus on clinical or translational cancer informatics. Manuscripts were then selected based on their methodological rigor, scientific impact, innovation, and contribution towards cancer informatics as a field or on their impact on cancer care and research. Results: Key developments and opportunities in cancer informatics research in the areas of digital health, machine learning, and precision oncology were summarized. Conclusion: While there are numerous innovations in the field of cancer informatics to advance prevention and clinical care, considerable challenges remain related to data sharing and privacy, digital accessibility, and algorithm biases and interpretation. The implementation and application of these findings in cancer care necessitates further consideration and research.

show abstract

Section: Discussionmentioning

confidence: 99%

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

et al. 2020

View full text Add to dashboard Cite

show abstract

“…CryptoNets, by applying ML to the problem regarding medical, educational, financial, or other kinds of confidential data, requires not only accurate forecasts but also careful cares to keep them safe and secure [40]. CryptoNets is basically developed by the Microsoft Research group, by introducing levelled Homomorphic Encryption (LHC).…”

Section: Cryptonetsmentioning

confidence: 99%

A Review of Deep Learning Security and Privacy Defensive Techniques

Tariq

Memon

Ahmed

et al. 2020

Mobile Information Systems

View full text Add to dashboard Cite

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.

show abstract

“…While federated ML minimizes the amount of raw data to be shared or centralized, it can be considered as a tactic to preserve security and privacy. However, recent research shows that data leakage is theoretically still possible in such collaborative learning environments [9].…”

Section: Federated Machine Learning and Deep Learningmentioning

confidence: 99%

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

et al. 2018

View full text Add to dashboard Cite

The adoption of machine learning and deep learning is on the rise in the cybersecurity domain where these AI methods help strengthen traditional system monitoring and threat detection solutions. However, adversaries too are becoming more effective in concealing malicious behavior amongst large amounts of benign behavior data. To address the increasing time-to-detection of these stealthy attacks, interconnected and federated learning systems can improve the detection of malicious behavior by joining forces and pooling together monitoring data. The major challenge that we address in this work is that in a federated learning setup, an adversary has many more opportunities to poison one of the local machine learning models with malicious training samples, thereby influencing the outcome of the federated learning and evading detection. We present a solution where contributing parties in federated learning can be held accountable and have their model updates audited. We describe a permissioned blockchain-based federated learning method where incremental updates to an anomaly detection machine learning model are chained together on the distributed ledger. By integrating federated learning with blockchain technology, our solution supports the auditing of machine learning models without the necessity to centralize the training data. Experiments with a realistic intrusion detection use case and an autoencoder for anomaly detection illustrate that the increased complexity caused by blockchain technology has a limited performance impact on the federated learning, varying between 5 and 15%, while providing full transparency over the distributed training process of the neural network. Furthermore, our blockchain-based federated learning solution can be generalized and applied to more sophisticated neural network architectures and other use cases.

show abstract

Deep Models Under the GAN

Cited by 901 publications

References 30 publications

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

From Patient Engagement to Precision Oncology: Leveraging Informatics to Advance Cancer Care

A Review of Deep Learning Security and Privacy Defensive Techniques

Chained Anomaly Detection Models for Federated Learning: An Intrusion Detection Case Study

Contact Info

Product

Resources

About