A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorisation of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings. Interested readers are encouraged to access our repository at https://github.com/tamlhp/awesome-privex.

Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction

Advances in deep neural network (DNN) architectures have enabled new prediction techniques for stock market data. Unlike other multivariate time-series data, stock markets show two unique characteristics: (i) \emph{multi-order dynamics}, as stock prices are affected by strong non-pairwise correlations (e.g., within the same industry); and (ii) \emph{internal dynamics}, as each individual stock shows some particular behaviour. Recent DNN-based methods capture multi-order dynamics using hypergraphs, but rely on the Fourier basis in the convolution, which is both inefficient and ineffective. In addition, they largely ignore internal dynamics by adopting the same model for each stock, which implies a severe information loss. In this paper, we propose a framework for stock movement prediction to overcome the above issues. Specifically, the framework includes temporal generative filters that implement a memory-based mechanism onto an LSTM network in an attempt to learn individual patterns per stock. Moreover, we employ hypergraph attentions to capture the non-pairwise correlations. Here, using the wavelet basis instead of the Fourier basis, enables us to simplify the message passing and focus on the localized convolution. Experiments with US market data over six years show that our framework outperforms state-of-the-art methods in terms of profit and stability. Our source code and data are available at \url{https://github.com/thanhtrunghuynh93/estimate}.

A Survey of Machine Unlearning

Computer systems hold a large amount of personal data over decades. On the one hand, such data abundance allows breakthroughs in artificial intelligence (AI), especially machine learning (ML) models. On the other hand, it can threaten the privacy of users and weaken the trust between humans and AI. Recent regulations require that private information about a user can be removed from computer systems in general and from ML models in particular upon request (e.g. the "right to be forgotten"). While removing data from back-end databases should be straightforward, it is not sufficient in the AI context as ML models often "remember" the old data. Existing adversarial attacks proved that we can learn private membership or attributes of the training data from the trained models. This phenomenon calls for a new paradigm, namely machine unlearning, to make ML models forget about particular data. It turns out that recent works on machine unlearning have not been able to solve the problem completely due to the lack of common frameworks and resources. In this survey paper, we seek to provide a thorough investigation of machine unlearning in its definitions, scenarios, mechanisms, and applications. Specifically, as a categorical collection of state-of-the-art research, we hope to provide a broad reference for those seeking a primer on machine unlearning and its various formulations, design requirements, removal requests, algorithms, and uses in a variety of ML applications. Furthermore, we hope to outline key findings and trends in the paradigm as well as highlight new areas of research that have yet to see the application of machine unlearning, but could nonetheless benefit immensely. We hope this survey provides a valuable reference for ML researchers as well as those seeking to innovate privacy technologies. Our resources are at https://github.com/tamlhp/awesome-machine-unlearning.

Detecting Rumours with Latency Guarantees using Massive Streaming Data

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social networks. Hence, in this paper, we argue for best-effort rumour detection that detects most rumours quickly rather than all rumours with a high delay. To this end, we combine techniques for efficient, graph-based matching of rumour patterns with effective load shedding that discards some of the input data while minimising the loss in accuracy. Experiments with large-scale real-world datasets illustrate the robustness of our approach in terms of runtime performance and detection accuracy under diverse streaming conditions.

Link-Intensive Alignment for Incomplete Knowledge Graphs

Knowledge graph (KG) alignment - the task of recognizing entities referring to the same thing in different KGs - is recognized as one of the most important operations in the field of KG construction and completion. However, existing alignment techniques often assume that the input KGs are complete and isomorphic, which is not true due to the real-world heterogeneity in the domain, size, and sparsity. In this work, we address the problem of aligning incomplete KGs with representation learning. Our KG embedding framework exploits two feature channels: transitivity-based and proximity-based. The former captures the consistency constraints between entities via translation paths, while the latter captures the neighbourhood structure of KGs via attention guided relation-aware graph neural network. The two feature channels are jointly learned to exchange important features between the input KGs while enforcing the output representations of the input KGs in the same embedding space. Also, we develop a missing links detector that discovers and recovers the missing links in the input KGs during the training process, which helps mitigate the incompleteness issue and thus improve the compatibility of the learned representations. The embeddings then are fused to generate the alignment result, and the high-confidence matched node pairs are updated to the pre-aligned supervision data to improve the embeddings gradually. Empirical results show that our model is up to 15.2\% more accurate than the SOTA and is robust against different levels of incompleteness. We also demonstrate that the knowledge exchanging between the KGs helps reveal the unseen facts from knowledge graphs (a.k.a. knowledge completion), with the result being 3.5\% higher than the SOTA knowledge graph completion techniques.

A dual benchmarking study of facial forgery and facial forensics

In recent years, visual forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. In this paper, we present a benchmark that provides in-depth insights into visual forgery and visual forensics, using a comprehensive and empirical approach. More specifically, we develop an independent framework that integrates state-of-the-arts counterfeit generators and detectors, and measure the performance of these techniques using various criteria. We also perform an exhaustive analysis of the benchmarking results, to determine the characteristics of the methods that serve as a comparative reference in this never-ending war between measures and countermeasures.