Reviewerly/exHarmony huggingface dataset:
TBHM

Healthcare today is one of the most promising, prevailing, and sensitive sectors where patient information like prescriptions, health records, etc., are kept on the cloud to provide high quality on-demand services for enhancing e-health services by reducing the burden of data storage and maintenance to providing information independent of location and time. The major issue with healthcare organization is to provide protected sharing of healthcare data from the cloud to the decision makers, medical practitioners, data analysts, and insurance firms by maintaining confidentiality and integrity. This article proposes a novel and secure threshold based encryption scheme combined with homomorphic properties (TBHM) for accessing cloud based health information. Homomorphic encryption completely eliminates the possibility of any kind of attack as data cannot be accessed using any type of key. The experimental results report superiority of TBHM scheme over state of art in terms throughput, file encryption/decryption time, key generation time, error rate, latency time, and security overheads.
Geo-Location-Based File Security System for Healthcare Data

Nowadays, a person's medical information is just as important as their financial records as they may include not only names and addresses but also various sensitive data such as their employee details, bank account/credit card information, insurance details, etc. However, this fact is often overlooked when designing a file storage system for storing healthcare data. Storage systems are increasingly subject to attacks, so the security system is quickly becoming a mandatory feature of the data storage systems. For the purpose of security, we are dependent on various methods such as cryptographic techniques, two-step verification, and even biometric scanners. This chapter provides a mechanism to create a secure file storage system that provides two-layer security. The first layer is in the form of a password, through which the file is encrypted at the time of storage, and second is the locations at which the user wants the files to be accessed. Thus, this system would allow a user to access a file only at the locations specified by him/her. Therefore, the objective is to create a system that provides secure file storage based on geo-location information.
Assessing HIPAA Compliance of Open Source Electronic Health Record Applications

Electronic health record (EHR) applications are digital versions of paper-based patient health information. EHR applications are increasingly being adopted in many countries. They have resulted in improved quality in healthcare, convenient access to histories of patient medication and clinic visits, easier follow up of patient treatment plans, and precise medical decision-making process. The goal of this paper is to identify HIPAA technical requirements, evaluate two open source EHR applications (OpenEMR and OpenClinic) for security vulnerabilities using two open-source scanner tools (RIPS and PHP VulnHunter), and map the identified vulnerabilities to HIPAA technical requirements.
Near Data Processing in Taurus Database

Huawei's cloud-native database system GaussDB for MySQL (also known as Taurus) stores data in a separate storage layer consisting of a pool of storage servers. Each server has considerable compute power making it possible to push data reduction operations (selection, projection, and aggregation) close to storage. This paper describes the design and implementation of near data processing (NDP) in Taurus. NDP has several benefits: it reduces the amount of data shipped over the network; frees up CPU capacity in the compute layer; and reduces query run time, thereby enabling higher system throughput. Experiments with the TPC-H benchmark (100 GB) showed that 18 out of 22 queries benefited from NDP; data shipped was reduced by 63%; and CPU time by 50%. On Q15 the impact was even higher: data shipped was reduced by 98%; CPU time by 91%; and run time by 80%.
Distributed Set Label-Constrained Reachability Queries over Billion-Scale Graphs

Set label-constrained reachability (SLCR) query in edge-labeled graphs is a building block of many graph-based applications. Formally, given two sets <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$T$</tex> of source and target vertices and a label set (, it returns all reachable vertex pairs (s, t) under the constraint of (, where <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$s$</tex> ∊ <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> ∊T. There have been abundant index-based approaches to be applied to process the SLCR query. However, distributed approaches are desirable to process large-scale graphs because of the advantages of good scalability and real-time response. Now, there is no efficient distributed approach to the SLCR query. Most index-based approaches face limitations in terms of index construction and query performance when being extended to the distributed environment for processing large-scale graphs. To alleviate these problems, we first build a boundary graph-based index (BoundG) to reduce the time overhead of index construction. Consider the query performance of the BoundG-based approach has no noticeable improvement. We further construct a novel two layers 2-hop index (TL2hop), and a TL2hop-based query algorithm (TLQA) is designed by integrating an early termination strat-egy that reduces the communication overhead and boosts the query performance. Experimental results over eight data graphs demonstrate that the index time of BoundG is comparable to that of the state-of-the-art, and TL2hop significantly outperforms the state-of-the-art technique in terms of query response time (up to 4 orders of magnitude speedup).
Privacy-Preserving Collaborative Filtering Using Fully Homomorphic Encryption

In this paper, we propose privacy-preserving user-based CF protocols using the BGV fully homomorphic encryption scheme, named BGV-CF and optimized BGV-CF (OBGV-CF), in order to protect privacy of users in recommender systems. The proposed schemes are implemented by C++, and testbeds using the MovieLens dataset demonstrate that the proposed protocols successfully achieve their design goals.
Finding Critical Users in Social Communities via Graph Convolutions (Extended Abstract)

Finding critical users whose existence keeps a social community cohesive is an important problem in social networks. Considering a k-core community, finding critical users is to find a set of nodes U, with a given size b, in the community that maximizes the number of nodes to be deleted when nodes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$U$</tex> are deleted. The problem is NP-complete. The state-of-the-art algorithm is a greedy algorithm without a performance guaran-tee. To improve the performance, we propose a novel learning-based heuristic. A neural network model, Self-attentive Core Graph Convolution Network, SCGCN is learned for inference the criticalness of unseen node combinations. Furthermore, to reduce the inference space, we propose a deterministic strategy to prune unpromising nodes. Our experiments show that SCGCN signifi-cantly improves the quality of the solutions compared with the state-of-the-art algorithms.
A Model-Agnostic Approach for Learning with Noisy Labels of Arbitrary Distributions

Most real-world datasets contain label noise, which can negatively affect downstream ML models trained on them. To deal with this problem, one can clean the mislabeled data before training, which is not only time-consuming and expensive but also requires domain expertise. Another approach is to use a noise-robust ML training algorithm. However, existing methods have some prerequisites that may not be practical in many applications (e.g., they are tied to specific downstream model architecture or they are applicable to specific noise distributions). In this paper, we propose a model-agnostic approach for learning with noisy labels of arbitrary distributions. In particular, our approach can work with any gradient descent optimization based machine learning model and deal with any label noise distribution. We achieve them by proposing two theoretically grounded noise-robust loss functions (for different noise distributions), and we are able to automatically decide which loss function to use based on a novel noise setting detection module. We directly learn the required hyper-parameters in the loss functions via meta-learning technique to minimize the loss on a given small clean validation set, and propose several strategies to improve the efficiency of training. Experiments on multiple datasets with both real-world and injected label noise show that our method performs better than state-of-the-art approaches.
How Learning Can Help Complex Cyclic Join Decomposition

Recently, machine learning (ML) and deep learning (DL) techniques have been extensively studied in database systems including cardinality/selectivity estimation for optimizing queries with selections and joins. However, the issue of how to support complex cyclic join queries by ML/DL has not yet been well studied. An important research issue in optimizing complex cyclic join queries is how to decompose complex cyclic joins into a join tree where a node in the join tree may represent a subquery with cyclic joins. The main application of complex cyclic join queries is to support subgraph matching queries, which find matches of a user-given pattern graph in a large node/edge-labeled graph by subgraph isomorphism, when a graph is stored in a relational database system. Here, when a graph is stored in an edge table, the joins will be mainly self-joins. In the existing work, such decomposition is done by estimation with AGM bound. In this work, we demonstrate how ML/DL can support such complex cyclic self-joins by providing a more accurate estimation. We build a prototyped system, LSSMatch, based on ML/DL techniques, with a GUI to provide insights to observe how ML/DL-based techniques contribute to query optimization for complex cyclic self-join queries.
A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing (Extended Abstract)

In this paper, we propose a prefix-preserving encryption based data outsourcing framework which is applicable to multiple different types of data, such as geo-locations, market basket data, DNA sequences, numerical data and timestamps. It enables accurate data analyses on the encrypted data while ensuring strong privacy against inference attacks. The basic idea is to generates multiple indistinguishable data views in which one view fully preserves the utility for data analysis, and its accurate analysis result can be obliviously retrieved. We empirically evaluate the performance of our outsourcing framework against two common inference attacks on two different real datasets: the check-in location dataset and network traffic dataset, respectively. The experimental results demonstrate that our proposed framework preserves both privacy (with bounded leakage and indistinguishability of data views) and utility.
Academic Expert Finding via $(k, \mathcal{P})$-Core based Embedding over Heterogeneous Graphs

Finding relevant experts in specified areas is often crucial for a wide range of applications in both academia and industry. Given a user input query and a large amount of academic knowledge (e.g., academic papers), expert finding aims to find and rank the experts who are most relevant to the given query, from the academic knowledge. Existing studies mainly focus on the embedding-based solutions that (1) consider academic papers' textual semantic similarities to a given query through document representation models and (2) extract the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{top-}n$</tex> experts with the greatest similarities. Beyond the implicit textual semantics of papers, however, the papers' explicit relationships (e.g., co-authorship, citation, and same-topic relationship) in a heterogeneous academic graph (e.g., DBLP) are critical for document representation, insofar as they help improve the expert finding quality. Despite their importance, the explicit relationships of papers generally have been ignored in the literature. In this paper, we study the academic expert finding on heterogeneous graphs by considering the explicit relationships besides the implicit textual semantics of papers in one representation model. Specifically, we first define the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(k,\mathcal{P})$</tex> -core to denote a cohesive community of papers that are closely connected via a meta-path <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathcal{P}(\mathcal{P}$</tex> indicates the different relationships of papers). We then propose an offline <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(k,\mathcal{P})$</tex> -core based document embedding model to capture papers' various explicit relationships for representation. Moreover, by using papers' embeddings, we present an online threshold algorithm (TA)-based method to efficiently return top-n experts via a carefully designed proximity graph-based index (PG-Index). We extend our approach to support multiple relationships simultaneously for representation. Extensive experiments over real-world datasets demonstrate the effectiveness and efficiency of our approach.
VICS-GNN: A Visual Interactive System for Community Search via Graph Neural Network

Community Search, which locates the desired sub-graph containing the query node, is a fundamental operation in network analysis. Most of the existing systems rely on pre-defined rules to find the community, while we argue that the target community is always specific for different purposes and the pre-defined rules may not be suitable. In this work, we demonstrate VICS-GNN, a Visual Interactive system for Community Search via graph Neural Network. VICS-GNN provides end users with a flexible, user-friendly front end to manage and explore the sub-graph around the query node, allows users labeling nodes to guide G NN models in learning community rules by combining content and structural features, and locates the community interactively and iteratively. In the demonstration, demo visitors will be invited to experience the VICS-GNN system using real-world data from Wikipedia and Sina Weibo to feel how convenient and intuitive it is to help with community search.
ScaleG: A Distributed Disk-based System for Vertex-centric Graph Processing (Extended Abstract)

Designing disk-based distributed graph systems has drawn a lot of research due to the strong expressiveness of the graph model and rapidly increasing graph volume. However, several challenges still exist in achieving both high computational efficiency and low network communication under the limitation of memory. In this paper, we design a novel distributed disk-based graph processing system, ScaleG, with a series of user-friendly programming interfaces. We propose several techniques to reduce both disk I/Os in each machine and message I/Os via the network. We manage all messages in memory and bound the volume of all messages by the number of vertices. We also carefully design the data structure to support partial computation and automatic vertex activation. We conduct extensive experiments on real-world big graphs to show the high efficiency of our system.
Exploration of Knowledge Graphs via Online Aggregation

Exploration systems over large-scale RDF knowl-edge graphs often rely on aggregate count queries to indicate how many results the user can expect for the possible next steps of exploration. Such systems thus encounter a challenging computational problem: evaluating aggregate count queries efficiently enough to allow for interactive exploration. Given that precise results are not always necessary, a promising alternative is to apply online aggregation, where initially imprecise results converge towards more precise results over time. However, state-of-the-art online aggregation algorithms, such as Wander Join, fail to provide accurate results due to frequent rejected paths that slow convergence. We thus devise an algorithm for online aggregation that specializes in exploration queries on knowledge graphs; our proposal leverages the low dimension of RDF graphs, and the low selectivity of exploration queries, by augmenting random walks with exact partial computations using a worst-case optimal join algorithm. This approach reduces the number of rejected paths encountered while retaining a fast sample time. In an experimental study with random interactions exploring two large-scale knowledge graphs, our algorithm shows a clear reduction in error over time versus Wander Join.
Maximum Signed $\theta$-Clique Identification in Large Signed Graphs (Extended abstract)

Maximum clique identification is a fundamental problem for many domains. In real-world applications, signed information, e.g., friend and enemy, naturally exists in graphs. However, most existing research focuses on unsigned graph analysis. In this paper, we propose a new clique model for signed graphs, named signed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> -clique. We show that the problem of identifying the maximum signed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\theta$</tex> -clique is NP-hard. To scale for large signed graphs, novel pruning rules and search strategies are developed. Extensive experiments are conducted on 8 real-world graphs to verify the advantages of proposed model and techniques.
DRGI: Deep Relational Graph Infomax for Knowledge Graph Completion: (Extended Abstract)

Recently, many knowledge graph embedding models for knowledge graph completion have been proposed, ranging from the initial translation-based models such as TransE to recent convolutional neural network (CNN) models such as ConvE. However, these models only focus on semantic information of knowledge graph and neglect the natural graph structure information. Although graph convolutional network (GCN)-based models for knowledge graph embedding have been introduced to address this issue, they still suffer from fact incompleteness, resulting in the unconnectedness of knowledge graph. To solve this problem, we propose a novel model called deep relational graph infomax (DRGI) with mutual information (MI) maximization which takes the benefit of complete structure information and semantic information together. Specifically, the proposed DRGI consists of two encoders which are two identical adaptive relational graph attention networks (ARGATs), corresponding to catching semantic information and complete structure information respectively. Our method establishes new state-of-the-art on the standard datasets for knowledge graph completion.
Conditional Regression Rules

Mixed data distribution is widely observed, for example, the bird migration data consist of the observed locations of various birds in different years, varying in data distribution. Learning a single regression model over such a mixed data distribution is often ineffective, while manually segmenting the data, e.g., by bird, date or region, for learning individual models is truly labor-intensive. In this paper, we propose to automatically discover the regression models that apply conditionally to only a part of the data, namely conditional regression rules (CRRs), enlightened by the conditional functional dependencies (CFDs) that are FDs hold only in some data. Remarkably, a regression model may apply in different parts of data, e.g., the seasonal migration of birds is similar in different years. To capture the shared regression models, we investigate the inference of CRRs. An algorithm is devised to learn and discover CRRs from data, with the help of CRR inference. Extensive experiments on real-world datasets demonstrate that the discovered conditional regression rules are more effective than the regression models without conditions. In particular, with the inference of CRRs, the number of learned CRRs is significantly reduced without sacrificing rule semantics.
Neighbor-Anchoring Adversarial Graph Neural Networks (Extended Abstract)

While graph neural networks (GNNs) exhibit strong discriminative power, they often fall short of learning the underlying node distribution for increased robustness. To deal with this, inspired by generative adversarial networks (GANs), we investigate the problem of adversarial learning on graph neural networks, and propose a novel framework named NAGNN (i.e., Neighbor-anchoring Adversarial Graph Neural Networks) for graph representation learning, which trains not only a discriminator but also a generator that compete with each other. In particular, we propose a novel neighbor-anchoring strategy, where the generator produces samples with explicit features and neighborhood structures anchored on a reference real node, so that the discriminator can perform neighborhood aggregation on the fake samples to learn superior representations.
k-Pleased Querying (Extended Abstract)

<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -Regret Querying is a well studied problem to query a dataset <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$D$</tex> for a small subset <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> of size <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> with the minimal regret ratio for unknown utility functions. In this paper, we point out some issues in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> - Regret Querying, including the assumption of non-negative dataset and the lack of shift invariance. Known algorithms for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> - Regret Querying are limited in scope and result quality, and are based on the assumption of non-negative data. We introduce a new problem definition called k-pleased querying for dealing with the shift variance issue, and propose a strategy of random sampling of the utility functions. This strategy is based on a study of the theoretical guarantee of the sampling approach. We also introduce a dimensionality reduction strategy, an improved greedy algorithm, and a study of other utility function sampling methods. All of our solutions can handle negative data. Theoretically, we derive a guarantee on the approximation attained by our sampling algorithm. Experimental results on numerous real datasets show that our proposed method is effective even with a small number of samples and small values of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> .