Few-Shot Graph Classification with Structural-Enhanced Contrastive Learning for Graph Data Copyright Protection

Open-source licenses can promote the development of machine learning by allowing others to access,modify,and redistribute the training dataset.However,not all open-source licenses may be appropriate for data sharing,as some may not provide adequate protections for sensitive or personal information such as social network data.Additionally,some data may be subject to legal or regulatory restrictions that limit its sharing,regardless of the licensing model used.Hence,obtaining large amounts of labeled data can be difficult,time-consuming,or expensive in many real-world scenarios.Few-shot graph classification,as one application of meta-learning in supervised graph learning,aims to classify unseen graph types by only using a small amount of labeled data.However,the current graph neural network methods lack full usage of graph structures on molecular graphs and social network datasets.Since structural features are known to correlate with molecular properties in chemistry,structure information tends to be ignored with sufficient property information provided.Nevertheless,the common binary classification task of chemical compounds is unsuitable in the few-shot setting requiring novel labels.Hence,this paper focuses on the graph classification tasks of a social network,whose complex topology has an uncertain relationship with its nodes'attributes.With two multi-class graph datasets with large node-attribute dimensions constructed to facilitate the research,we propose a novel learning framework that integrates both meta-learning and contrastive learning to enhance the utilization of graph topological information.Extensive experiments demonstrate the competitive performance of our framework respective to other state-of-the-art methods.

A Novel Transparent and Auditable Fog-Assisted Cloud Storage with Compensation Mechanism

This paper introduces a new fog-assisted cloud storage which can achieve much higher throughput compared to the traditional cloud-only storage architecture by reducing the traffics toward the cloud storage. The fog-storage service providers are transparency to end-users and therefore, no modification on the end-user devices is necessary. This new system is featured with(1) a stronger audit scheme which is naturally coupled with the proposed architecture and does not suffer from the replay attack and(2) a transparent and efficient compensation mechanism for the fog-storage service providers. We provide rigorous theoretical analysis on the correctness and soundness of the proposed system. To the best of our knowledge, this is the first paper to discuss about a storage data audit scheme for fog-assisted cloud storage as well as the compensation mechanism for the service providers of the fog-storage service providers.

Link-Privacy Preserving Graph Embedding Data Publication with Adversarial Learning

The inefficient utilization of ubiquitous graph data with combinatorial structures necessitates graph embedding methods,aiming at learning a continuous vector space for the graph,which is amenable to be adopted in traditional machine learning algorithms in favor of vector representations.Graph embedding methods build an important bridge between social network analysis and data analytics,as social networks naturally generate an unprecedented volume of graph data continuously.Publishing social network data not only brings benefit for public health,disaster response,commercial promotion,and many other applications,but also gives birth to threats that jeopardize each individual’s privacy and security.Unfortunately,most existing works in publishing social graph embedding data only focus on preserving social graph structure with less attention paid to the privacy issues inherited from social networks.To be specific,attackers can infer the presence of a sensitive relationship between two individuals by training a predictive model with the exposed social network embedding.In this paper,we propose a novel link-privacy preserved graph embedding framework using adversarial learning,which can reduce adversary’s prediction accuracy on sensitive links,while persevering sufficient non-sensitive information,such as graph topology and node attributes in graph embedding.Extensive experiments are conducted to evaluate the proposed framework using ground truth social network datasets.

An unsupervised anomaly detection framework for detecting anomalies in real time through network system’s log files analysis

Nowadays,in almost every computer system,log files are used to keep records of occurring events.Those log files are then used for analyzing and debugging system failures.Due to this important utility,researchers have worked on finding fast and efficient ways to detect anomalies in a computer system by analyzing its log records.Research in log-based anomaly detection can be divided into two main categories:batch log-based anomaly detection and streaming log-based anomaly detection.Batch log-based anomaly detection is computationally heavy and does not allow us to instantaneously detect anomalies.On the other hand,streaming anomaly detection allows for immediate alert.However,current streaming approaches are mainly supervised.In this work,we propose a fully unsupervised framework which can detect anomalies in real time.We test our framework on hdfs log files and successfully detect anomalies with an F-1 score of 83%.

Secure verifiable aggregation for blockchain-based federated averaging

IoT devices’storage and computation capacities are constantly increasing in recent years,which brings critical challenges in data privacy protection.Federated learning(FL)and blockchain technology are two popular tech-niques used in IoT data aggregation,where FL enables data training with privacy protection,and blockchain provides a decentralized architecture for data storage and mining.However,very few the state-of-the-art works consider the applicability of the combination of FL and blockchain.In this paper,we adopt the federated aver-aging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance.We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy.Finally,we propose and implement a non-interactive Public Verifiable Secret Sharing(PVSS)algorithm with Distributed Hash Table(DHT)that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users.At last,we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

Lightweight Super-Resolution Model for Complete Model Copyright Protection

Deep learning based techniques are broadly used in various applications, which exhibit superior performance compared to traditional methods. One of the mainstream topics in computer vision is the image super-resolution task. In recent deep learning neural networks, the number of parameters in each convolution layer has increased along with more layers and feature maps, resulting in better image super-resolution performance. In today’s era, numerous service providers offer super-resolution services to users, providing them with remarkable convenience. However, the availability of open-source super-resolution services exposes service providers to the risk of copyright infringement, as the complete model could be vulnerable to leakage. Therefore, safeguarding the copyright of the complete model is a non-trivial concern. To tackle this issue, this paper presents a lightweight model as a substitute for the original complete model in image super-resolution. This research has identified smaller networks that can deliver impressive performance, while protecting the original model’s copyright. Finally, comprehensive experiments are conducted on multiple datasets to demonstrate the superiority of the proposed approach in generating super-resolution images even using lightweight neural networks.