This paper presents a comprehensive framework for understanding the properties of nodes in a weighted network related to an algorithmic process based on counterfactuals.The framework is applied to the problem of community detection to demonstrate its general applicability.It identifies counterfactual explanations, revealing which network connections, when modified by changing their weights, would cause a node to lose its community affiliation, answering questions like "Why does node v belong to community C?".The core contribution lies in providing an interpretable and actionable framework for understanding and manipulating network structures. Index Terms-networks, counterfactual explanations, community detection• Assume for the network G = (V, E, w) it holds that v ∈ P. If for G ′ = (V, E, w ′ ), where only w e is changed

In a wide spectrum of real-world applications, it is very important to analyze and mine graph data such as social networks, communication networks, citation networks, and so on. However, the release of such graph data often raises privacy issue, and the graph privacy preservation has recently drawn much attention from the database community. While prior works on graph privacy preservation mainly focused on protecting the privacy of either the graph structure only or vertex attributes only, in this paper, we propose a novel mechanism for graph privacy preservation by considering attacks from both graph structures and vertex attributes, which transforms the original graph to a so-called$kt$-safe graph, via$k$-anonymity and$t$-closeness. We prove that the generation of a$kt$-safe graph is NP-hard, therefore, we propose a feasible framework for effectively and efficiently anonymizing a graph with low anonymization cost. In particular, we design a cost-model-based graph partitioning approach to enable our proposed divide-and-conquer strategy for the graph anonymization, and propose effective optimization techniques such as pruning method and a tree synopsis to improve the anonymization efficiency over large-scale graphs. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed$kt$-safe graph generation approach on both real and synthetic data sets.

More and more hospitals are now deploying mul-tiple cameras in trauma room for a multi-perspective remote observation. but video surveillance system can cause privacy breach by showing and storing sensitive information of patients and staff. We use OpenPose which is a state-of-the-art human body skeletons estimation framework to extract 18 human key skeleton points. For privacy preservation, we can apply the image obfuscation techniques to human heads, we also can use human skeleton to replace the human body in the truth background. we proposed a head detection method based on the 5 key points of each head output from OpenPose. We applied the st-gcn algorithm to recognize human actions, we propose a interactive algorithm for multiple cameras to recognize and trace the same person in different cameras, Based on multi-view action recognition for the same person, we can take action recognition accuracy to a high level. Our experiment results prove that our proposed technique has a high performance in privacy protection applications. Now we focus on the interactive algorithm for multiple cameras.

In many real applications such as the data integration, social network analysis, and the Semantic Web, the entity resolution (ER) is an important and fundamental problem, which identifies and links the same real-world entities from various data sources. While prior works usually consider ER over static and complete data, in practice, application data are usually collected in a streaming fashion, and often incur missing attributes (due to the inaccuracy of data extraction techniques). Therefore, in this paper, we will formulate and tackle a novel problem, topic-aware entity resolution over incomplete data streams (TER-iDS), which online imputes incomplete tuples and detects pairs of topic-related matching entities from incomplete data streams. In order to effectively and efficiently tackle the TER-iDS problem, we propose an effective imputation strategy, carefully design effective pruning strategies, as well as indexes/synopsis, and develop an efficient TER-iDS algorithm via index joins. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of our proposed TER-iDS approach over real data sets.

For decades, the join operator over fast data streams has always drawn much attention from the database community, due to its wide spectrum of real-world applications, such as online clustering, intrusion detection, sensor data monitoring, and so on. Existing works usually assume that the underlying streams to be joined are complete (without any missing values). However, this assumption may not always hold, since objects from streams may contain some missing attributes, due to various reasons such as packet losses, network congestion/failure, and so on. In this paper, we formalize an important problem, namely join over incomplete data streams (Join-iDS), which retrieves joining object pairs from incomplete data streams with high confidences. We tackle the Join-iDS problem in the style of "data imputation and query processing at the same time". To enable this style, we design an effective and efficient cost-model-based imputation method via deferential dependency (DD), devise effective pruning strategies to reduce the Join-iDS search space, and propose efficient algorithms via our proposed cost-model-based data synopsis/indexes. Extensive experiments have been conducted to verify the efficiency and effectiveness of our proposed Join-iDS approach on both real and synthetic data sets.

No Reference Image Quality Assessment (NR-IQA) refers to algorithms that predict the quality of distorted image where the reference image is not available. NR-IQA algorithms are divided into two categories: specific distortion quality assessment and general purpose quality assessment. The first type of algorithms deals with a specific distortion and predict the quality of the image based on this distortion. Which means it assumes that the distortion in the image is known. On the other hand, the general purpose quality assessment type predicts the quality of image with no information about the distortion that affecting the tested image. A two-stage framework, is proposed by Moorthy and Bovik [1], which classifies the distortion followed by distortion-specific quality assessment method. Our proposal here is to improve the classification portion by investigating the performance of different classification techniques and different features. Each feature is validated using different features evaluation techniques. As a result, we construct a set of optimal features that classify image distortions with a high accuracy rate.

Biometric data are the sensitive personal information and the large intra-class variability due to changes of the environment conditions is an issue in these type of data. Adaptive biometric is the solution that has been introduced and can make the systems more accurate and reliable. For this purpose, semi-supervised learning has been shown to be a possible strategy. On the other hand, one problem in semi-supervised learning is selecting the decision threshold for adaption which can make the strategy unstable. In particular, a strong classifier, in a multimodal system, is better if adapted threshold is replaced with an inflexible one. This paper presents a fuzzy system to find the better threshold for adaptation. Experiments on MOBIO face and speech database show that the proposed strategy is a better approach in comparison to normal adaptive method.

To protect user's information, computer systems utilize access control models. These models are supported by a set of policies defined by security administrators in the environment where the organization is active. In previous studies it has been shown that building a user interface that dynamically changes with the security policies defined for each user is a cumbersome task. This work is a further expansion of an improved dynamic model that adjusts users' security policies based on the level of trust that they hold. We use machine learning beside the trust manager component that helps the system to adapt itself, learn from the user's behavior and recognize access patterns based on the similar access requests and not only limit the illegitimate access, but also predict and prevent potential malicious and questionable accesses.

This paper describes our study of the incidence of Personal Health Information (PHI) on the Web. PHI is usually shared under conditions of confidentiality, protection and trust, and should not be disclosed or available to unrelated third parties or the general public. We first analyzed the characteristics that potentially make systems successful in identification of unsolicited or unjustified PHI disclosures. In the next stage, we designed and implemented an integrated Natural Language Processing/Machine Learning (NLP/ML)-based system that detects disclosures of personal health information, specifically according to the above characteristics including detected patterns. This research is regarded as the first step toward a learning system that will be trained based on a limited training set built on the result of the processing chain described in the paper in order to generally detect the PHI disclosures over the web.

This is a preliminary work to extend SQL to support user privacy in social networks. The proposal is to extend the data definition and data manipulation languages to capture privacy-preserved mandatory and discretionary access controls, respectively. Here, we focus on common user privacy requirements, such as purpose, generalization, and retention, used by social networks desiring to support privacy. Hence, each user can discretionarily control the set of privileges over the view representing their profile. We plan to support the extended language with underlying catalogues, algorithms, and prototypes. The objective is to develop a low-cost mechanism to preserve privacy in databases,with applications in social networks, e-health, e-business, e-government, etc.

Privacy is a leading concern for anyone that utilizes computing resources whether shopping on the Internet or visiting their doctor. Legislative acts require enterprises and data collectors to protect the privacy of their customers and data owners. Although privacy policy frameworks such as P3P assist data collectors in demonstrating their privacy policies to customers (i.e. publishing privacy policy on Web sites), insufficient research has been reported to help users visualize privacy policies. This paper presents a privacy policy visualization model based on the predicates of a privacy policy model. The key contribution is to provide a visualization model that facilitates understanding the policies for the data owners and provides the opportunity for the policy officers to better understand the designed policies. Finally, we demonstrate the model with a use case drawn from the policies of an online social network.

A number of graph partitioners are currently available for solving linear systems on parallel computers. Partitioning algorithms divide the graph that arises from the linear system into a specified number of partitions such that the workload per processor is balanced and the communication between the processors is minimized. The measure of partition quality is often taken to be the number of edges cut by the partition. Ultimately the quality of a partition will be reflected in the execution time of the parallel application. In this paper, we introduce a linear solver benchmark that enables comparison of partition quality. This work also serves to motivate further work on developing benchmarks for graph partitioners.