When machine learning meets privacy: A survey and outlook
The newly emerged machine learning (eg, deep learning) methods have become a strong
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
driving force to revolutionize a wide range of industries, such as smart healthcare, financial …
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
In the last few years, Artificial Intelligence (AI) has achieved a notable momentum that, if
harnessed appropriately, may deliver the best of expectations over many application sectors …
harnessed appropriately, may deliver the best of expectations over many application sectors …
[HTML][HTML] Explainable AI (XAI): A systematic meta-survey of current challenges and future opportunities
The past decade has seen significant progress in artificial intelligence (AI), which has
resulted in algorithms being adopted for resolving a variety of problems. However, this …
resulted in algorithms being adopted for resolving a variety of problems. However, this …
Trustworthy LLMs: A survey and guideline for evaluating large language models' alignment
Ensuring alignment, which refers to making models behave in accordance with human
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
intentions [1, 2], has become a critical task before deploying large language models (LLMs) …
Reconstructing training data from trained neural networks
Understanding to what extent neural networks memorize training data is an intriguing
question with practical and theoretical implications. In this paper we show that in some …
question with practical and theoretical implications. In this paper we show that in some …
Exploiting unintended feature leakage in collaborative learning
Collaborative machine learning and related techniques such as federated learning allow
multiple participants, each with his own training dataset, to build a joint model by training …
multiple participants, each with his own training dataset, to build a joint model by training …
A survey of privacy attacks in machine learning
As machine learning becomes more widely used, the need to study its implications in
security and privacy becomes more urgent. Although the body of work in privacy has been …
security and privacy becomes more urgent. Although the body of work in privacy has been …
Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models
Machine learning (ML) has become a core component of many real-world applications and
training data is a key factor that drives current progress. This huge success has led Internet …
training data is a key factor that drives current progress. This huge success has led Internet …
Differentially private federated learning: A client level perspective
Federated learning is a recent advance in privacy protection. In this context, a trusted curator
aggregates parameters optimized in decentralized fashion by multiple clients. The resulting …
aggregates parameters optimized in decentralized fashion by multiple clients. The resulting …
Knockoff nets: Stealing functionality of black-box models
Abstract Machine Learning (ML) models are increasingly deployed in the wild to perform a
wide range of tasks. In this work, we ask to what extent can an adversary steal functionality …
wide range of tasks. In this work, we ask to what extent can an adversary steal functionality …