ORCID Profile
0000-0001-7353-4159
Current Organisations
University of Tasmania
,
University of Melbourne
,
Macquarie University
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Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2022
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/6238607
Publisher: IEEE
Date: 08-2017
Publisher: IEEE
Date: 11-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2017
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Wiley
Date: 21-05-2019
DOI: 10.1002/CPE.5318
Publisher: IEEE
Date: 07-2022
Publisher: ACM
Date: 24-10-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2021
Publisher: Elsevier BV
Date: 08-2013
Publisher: IEEE
Date: 08-2018
Publisher: IEEE
Date: 2009
DOI: 10.1109/ISPA.2009.81
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2017
Publisher: IEEE
Date: 13-10-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2021
Publisher: Wiley
Date: 18-06-2020
DOI: 10.1002/SPE.2847
Abstract: Currently, smart farming has been established to realize agriculture automation by leveraging sensors to gather the growth and environmental data for crops, and realizing multiple intelligent controls, such as irrigation, fertilization, and so on, to increase the crop yields. To support real‐time intelligent controls, edge computing is introduced to smart farming by endowing computing and storage capacities to edge devices nearby the geographically distributed sensors. However, the farmers are relatively willing to purchase and deploy a small quantity of edge servers (ESs) in the farm from the perspective of expenditure saving, thereby leading to a key challenge to guarantee the performance of the real‐time controls and the overall edge services. In view of this challenge, a service offloading‐oriented ES placement method for supporting smart farming, called SOP, is proposed to optimize the data transmission delay from sensors to ESs and the load balance among ESs. More precisely, the corresponding service range of a certain ES is ascertained according to the specific analysis of the farming service requirements. Subsequently, the layout policies for the trade‐offs of the ES performance and service efficiency are acquired. Then the most balanced policy is determined as the final ES placement strategy. Eventually, we evaluate the performance of the whole ES system and the service execution efficiency with SOP.
Publisher: IEEE
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Elsevier BV
Date: 07-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-04-2021
Publisher: Springer Science and Business Media LLC
Date: 22-06-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-07-2021
Publisher: Elsevier BV
Date: 11-2018
Publisher: Elsevier BV
Date: 05-2020
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.164
Publisher: IEEE
Date: 12-2013
DOI: 10.1109/CSE.2013.163
Publisher: IEEE
Date: 08-2021
Publisher: Springer Berlin Heidelberg
Date: 04-09-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Springer Science and Business Media LLC
Date: 20-06-2022
Publisher: American Scientific Publishers
Date: 02-2015
Publisher: Springer Science and Business Media LLC
Date: 17-05-2019
Publisher: IEEE
Date: 07-2023
Publisher: Springer Science and Business Media LLC
Date: 09-02-2014
Publisher: Springer International Publishing
Date: 2019
Publisher: Springer International Publishing
Date: 2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2018
Publisher: IEEE
Date: 12-2021
Publisher: Wiley
Date: 13-07-2016
DOI: 10.1002/CPE.3909
Publisher: Springer International Publishing
Date: 2021
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 10-2018
Publisher: IEEE
Date: 18-07-2021
Publisher: IEEE
Date: 10-2021
Publisher: Elsevier BV
Date: 02-2016
Publisher: IEEE
Date: 11-2012
DOI: 10.1109/CGC.2012.43
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 11-2012
DOI: 10.1109/CGC.2012.47
Publisher: Springer Science and Business Media LLC
Date: 19-11-0011
Publisher: Springer International Publishing
Date: 2021
Publisher: Hindawi Limited
Date: 25-03-2021
DOI: 10.1002/INT.22412
Publisher: Springer Nature Singapore
Date: 2022
Publisher: Wiley
Date: 02-05-2011
DOI: 10.1002/CPE.1738
Publisher: Hindawi Limited
Date: 23-05-2019
DOI: 10.1155/2019/4529757
Publisher: ACM
Date: 21-10-2023
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 09-2021
Publisher: IEEE
Date: 09-2013
DOI: 10.1109/CGC.2013.24
Publisher: Elsevier BV
Date: 08-2020
Publisher: Wiley
Date: 03-06-2016
DOI: 10.1002/SPE.2412
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2014
Publisher: ACM
Date: 26-10-2021
Publisher: Springer International Publishing
Date: 2020
Publisher: Elsevier BV
Date: 08-2014
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/DASC.2011.79
Publisher: IEEE
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: ACM
Date: 17-10-2022
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2021
DOI: 10.1145/3417293
Abstract: With the ever-increasing prosperity of web Application Programming Interface (API) sharing platforms, it is becoming an economic and efficient way for software developers to design their interested mashups through web API re-use. Generally, a software developer can browse, evaluate, and select his or her preferred web APIs from the API's sharing platforms to create various mashups with rich functionality. The big volume of candidate APIs places a heavy burden on software developers’ API selection decisions. This, in turn, calls for the support of intelligent API recommender systems. However, existing API recommender systems often face two challenges. First, they focus more on the functional accuracy of APIs while neglecting the APIs’ actual compatibility. This then creates incompatible mashups. Second, they often require software developers to input a set of keywords that can accurately describe the expected functions of the mashup to be developed. This second challenge tests partial developers who have little background knowledge in the fields. To tackle the above-mentioned challenges, in this article we propose a compatibility-aware and text description-driven web API recommendation approach (named WAR text ). WAR text guarantees the compatibility among the recommended APIs by utilizing the APIs’ composition records produced by historical mashup creations. Besides, WAR text entitles a software developer to type a simple text document that describes the expected mashup functions as input. Then through textual description mining, WAR text can precisely capture the developers’ functional requirements and then return a set of APIs with the highest compatibility. Finally, through a real-world mashup dataset ProgrammableWeb, we validate the feasibility of our novel approach.
Publisher: Springer Singapore
Date: 2017
Publisher: Springer International Publishing
Date: 2016
Publisher: Informa UK Limited
Date: 21-10-2022
Publisher: Elsevier BV
Date: 08-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2019
Publisher: Springer Science and Business Media LLC
Date: 12-05-2022
DOI: 10.1007/S11280-022-01052-Z
Abstract: Anomaly detection plays a crucial role in many Internet of Things (IoT) applications such as traffic anomaly detection for smart transportation and medical diagnosis for smart healthcare. With the explosion of IoT data, anomaly detection on data streams raises higher requirements for real-time response and strong robustness on large-scale data arriving at the same time and various application fields. However, existing methods are either slow or application-specific. Inspired by the edge computing and generic anomaly detection technique, we propose an isolation forest based framework with dynamic Insertion and Deletion schemes (IDForest), which can incrementally update the forest to detect anomalies on data streams. Besides, IDForest is deployed on edge servers in parallel through packing each tree into a subtask, which facilitates the fast anomaly detection on data streams. Extensive experiments on both synthetic and real-life datasets demonstrate the efficiency and robustness of our framework for anomaly detection.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2018
Publisher: IEEE
Date: 11-2018
Publisher: Elsevier BV
Date: 10-2021
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 08-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: MDPI AG
Date: 30-01-2020
DOI: 10.3390/APP10030909
Abstract: With significant development of sensors and Internet of things (IoT), researchers nowadays can easily know what happens in water ecosystem by acquiring water images. Essentially, growing data category and size greatly contribute to solving water pollution problems. In this paper, we focus on classifying water images to sub-categories of clean and polluted water, thus promoting instant feedback of a water pollution monitoring system that utilizes IoT technology to capture water image. Due to low inter-class and high intra-class differences of captured water images, water image classification is challenging. Inspired by the ability to extract highly distinguish features of Convolutional Neural Network (CNN), we aim to construct an attention neural network for IoT captured water images classification that appropriately encodes channel-wise and multi-layer properties to accomplish feature representation enhancement. During construction, we firstly propose channel-wise attention gate structure and then utilize it to construct a hierarchical attention neural network in local and global sense. We carried out comparative experiments on an image dataset about water surface with several studies, which showed the effectiveness of the proposed attention neural network for water image classification. We applied the proposed neural network as a key part of a water image based pollution monitoring system, which helps users to monitor water pollution breaks in real-time and take instant actions to deal with pollution.
Publisher: IEEE
Date: 13-10-2022
Publisher: ACM
Date: 26-10-2021
Publisher: Informa UK Limited
Date: 05-05-2023
Publisher: Wiley
Date: 02-2023
DOI: 10.1111/COIN.12569
Publisher: Springer Science and Business Media LLC
Date: 10-07-2019
Publisher: Wiley
Date: 25-01-2013
DOI: 10.1002/CPE.2989
Publisher: Association for Computing Machinery (ACM)
Date: 13-10-2022
DOI: 10.1145/3501810
Abstract: Through the collaboration of cloud and edge, cloud-edge computing allows the edge that approximates end-users undertakes those non-computationally intensive service processing of the cloud, reducing the communication overhead and satisfying the low latency requirement of Internet of Vehicle (IoV). With cloud-edge computing, the computing tasks in IoV is able to be delivered to the edge servers (ESs) instead of the cloud and rely on the deployed services of ESs for a series of processing. Due to the storage and computing resource limits of ESs, how to dynamically deploy partial services to the edge is still a puzzle. Moreover, the decision of service deployment often requires the transmission of local service requests from ESs to the cloud, which increases the risk of privacy leakage. In this article, a method for privacy-aware IoV service deployment with federated learning in cloud-edge computing, named PSDF, is proposed. Technically, federated learning secures the distributed training of deployment decision network on each ES by the exchange and aggregation of model weights, avoiding the original data transmission. Meanwhile, homomorphic encryption is adopted for the uploaded weights before the model aggregation on the cloud. Besides, a service deployment scheme based on deep deterministic policy gradient is proposed. Eventually, the performance of PSDF is evaluated by massive experiments.
Publisher: Springer International Publishing
Date: 2022
Publisher: Association for Computing Machinery (ACM)
Date: 21-04-2021
DOI: 10.1145/3401979
Abstract: The Internet of Vehicles (IoV) connects vehicles, roadside units (RSUs) and other intelligent objects, enabling data sharing among them, thereby improving the efficiency of urban traffic and safety. Currently, collections of multimedia content, generated by multimedia surveillance equipment, vehicles, and so on, are transmitted to edge servers for implementation, because edge computing is a formidable paradigm for accommodating multimedia services with low-latency resource provisioning. However, the uneven or discrete distribution of the traffic flow covered by edge servers negatively affects the service performance (e.g., overload and underload) of edge servers in multimedia IoV systems. Therefore, how to accurately schedule and dynamically reserve proper numbers of resources for multimedia services in edge servers is still challenging. To address this challenge, a traffic flow prediction driven resource reservation method, called TripRes, is developed in this article. Specifically, the city map is ided into different regions, and the edge servers in a region are treated as a “big edge server” to simplify the complex distribution of edge servers. Then, future traffic flows are predicted using the deep spatiotemporal residual network (ST-ResNet), and future traffic flows are used to estimate the amount of multimedia services each region needs to offload to the edge servers. With the number of services to be offloaded in each region, their offloading destinations are determined through latency-sensitive transmission path selection. Finally, the performance of TripRes is evaluated using real-world big data with over 100M multimedia surveillance records from RSUs in Nanjing China.
Publisher: Elsevier BV
Date: 04-2020
Publisher: ACM
Date: 19-10-2020
Publisher: IEEE
Date: 12-2011
DOI: 10.1109/DASC.2011.98
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2014
Publisher: Springer International Publishing
Date: 2022
Publisher: Springer Singapore
Date: 2017
Publisher: ACM
Date: 21-10-2023
Publisher: Association for Computing Machinery (ACM)
Date: 03-2023
DOI: 10.1145/3532091
Abstract: In the era of smart healthcare tremendous growth, plenty of smart devices facilitate cognitive computing for the purposes of lower cost, smarter diagnostic, etc. Android system has been widely used in the field of IoMT, and as the main operating system. However, Android malware is becoming one major security concern for healthcare, by the serious threat for our medical software assets, like the leakage of private information, the abusing of critical operations, etc. Unfortunately, the existing methods focus on building sustainable classification models, without fully considering system API which is the key to model aging. Compared to the traditional methods, we apply the lifeCycle of API as temporal metric. In addition to the temporal view, the “sizes” of the APPs are utilized as spatial metric in the spatial view. Based on this, we firstly discuss the temporal and spatial metrics together in terms of clustering, and then propose our novel framework-TSDroid. In this framework, we use TS-based clustering algorithm to obtain clustering subsets to enhance the detection capability. We have carried out an experimental verification on three existing excellent methods (i.e., Drebin, HinDroid, and DroidEvolver) and obtain good promotion effects by our framework.
Publisher: IEEE
Date: 05-2020
Publisher: Elsevier BV
Date: 07-2013
Publisher: Wiley
Date: 10-09-2019
DOI: 10.1002/SPE.2749
Abstract: Edge computing (EC) emerges as a novel computing paradigm to offload computing tasks from user equipments (UEs) to edge notes (ENs) in fifth‐generation networks, which definitely breaks the resource limitation of UEs to a certain degree. However, it is troublesome to guarantee the overall operating performance of ENs due to the uneven distributed resource demands of UEs, the resulting transmission delay and the data loss for computation offloading between the covered EN and the deployed destination EN. In view of this challenge, a blockchain‐based computation offloading method, named BCO, is proposed in this paper. Technically, since blockchain is a promising technique for the decentralized system, a blockchain‐based EC framework is designed to degrade the data loss possibility by integrating blockchain and EC. Then, the nondominated sorting genetic algorithm, the third version (NSGA‐III), is leveraged to acquire the balanced offloading strategies. Furthermore, by taking advantage of Simple Additive Weighting and Multiple Criteria Decision Making, the optimal offloading strategy is identified. Finally, systematic experiments and analyses on the comparative experiment are conducted to verify the efficiency of our proposed method BCO.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2022
Publisher: Springer Nature Singapore
Date: 12-11-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 15-08-2021
Publisher: Association for Computing Machinery (ACM)
Date: 31-01-2022
DOI: 10.1145/3523273
Abstract: Machine learning (ML) models have been widely applied to various applications, including image classification, text generation, audio recognition, and graph data analysis. However, recent studies have shown that ML models are vulnerable to membership inference attacks (MIAs), which aim to infer whether a data record was used to train a target model or not. MIAs on ML models can directly lead to a privacy breach. For ex le, via identifying the fact that a clinical record that has been used to train a model associated with a certain disease, an attacker can infer that the owner of the clinical record has the disease with a high chance. In recent years, MIAs have been shown to be effective on various ML models, e.g., classification models and generative models. Meanwhile, many defense methods have been proposed to mitigate MIAs. Although MIAs on ML models form a newly emerging and rapidly growing research area, there has been no systematic survey on this topic yet. In this article, we conduct the first comprehensive survey on membership inference attacks and defenses. We provide the taxonomies for both attacks and defenses, based on their characterizations, and discuss their pros and cons. Based on the limitations and gaps identified in this survey, we point out several promising future research directions to inspire the researchers who wish to follow this area. This survey not only serves as a reference for the research community but also provides a clear description for researchers outside this research domain. To further help the researchers, we have created an online resource repository, which we will keep updated with future relevant work. Interested readers can find the repository at github.com/HongshengHu/membership-inference-machine-learning-literature.
Publisher: ACM
Date: 31-01-2017
Publisher: Springer International Publishing
Date: 2017
Publisher: Elsevier BV
Date: 09-2012
Publisher: IEEE
Date: 06-2017
DOI: 10.1109/ICWS.2017.15
Publisher: IEEE
Date: 12-2018
Publisher: Elsevier BV
Date: 07-2019
Publisher: Elsevier BV
Date: 12-2018
Publisher: IEEE
Date: 05-2018
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2023
Publisher: Springer International Publishing
Date: 2019
Publisher: Elsevier BV
Date: 07-2023
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 07-2013
Publisher: MDPI AG
Date: 10-09-2018
DOI: 10.3390/S18093030
Abstract: With the development of the Internet of Things (IoT) technology, a vast amount of the IoT data is generated by mobile applications from mobile devices. Cloudlets provide a paradigm that allows the mobile applications and the generated IoT data to be offloaded from the mobile devices to the cloudlets for processing and storage through the access points (APs) in the Wireless Metropolitan Area Networks (WMANs). Since most of the IoT data is relevant to personal privacy, it is necessary to pay attention to data transmission security. However, it is still a challenge to realize the goal of optimizing the data transmission time, energy consumption and resource utilization with the privacy preservation considered for the cloudlet-enabled WMAN. In this paper, an IoT-oriented offloading method, named IOM, with privacy preservation is proposed to solve this problem. The task-offloading strategy with privacy preservation in WMANs is analyzed and modeled as a constrained multi-objective optimization problem. Then, the Dijkstra algorithm is employed to evaluate the shortest path between APs in WMANs, and the nondominated sorting differential evolution algorithm (NSDE) is adopted to optimize the proposed multi-objective problem. Finally, the experimental results demonstrate that the proposed method is both effective and efficient.
Publisher: IEEE
Date: 11-2020
Publisher: IEEE
Date: 07-2011
DOI: 10.1109/ICWS.2011.11
Publisher: Elsevier BV
Date: 06-2019
Publisher: IEEE
Date: 12-2022
DOI: 10.1109/SMARTWORLD-UIC-ATC-SCALCOM-DIGITALTWIN-PRICOMP-METAVERSE56740.2022.00117
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2015
Publisher: Wiley
Date: 07-11-2014
DOI: 10.1002/CPE.3426
Publisher: Springer International Publishing
Date: 2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2016
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Date: 26-06-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2014
DOI: 10.1109/TPDS.2013.48
Publisher: IEEE
Date: 10-2018
Publisher: Hindawi Limited
Date: 11-10-2021
DOI: 10.1002/INT.22710
Publisher: Springer International Publishing
Date: 2018
Publisher: Springer Singapore
Date: 2020
Publisher: Springer Berlin Heidelberg
Date: 2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2020
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Springer Berlin Heidelberg
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: IEEE
Date: 04-2017
DOI: 10.1109/ICDE.2017.32
Publisher: IEEE
Date: 09-2010
DOI: 10.1109/HPCC.2010.20
Publisher: IEEE
Date: 12-2021
Publisher: IGI Global
Date: 2012
Abstract: In service selection, an end user often has his or her personal preferences imposing on a candidate service’s non-functional properties. For a service selection process promoted by a group of users, candidate services are often evaluated by a group of end users who may have different preferences or priorities. In this situation, it is often a challenging effort to make a tradeoff among various preferences or priorities of the users. In view of this challenge, a multi-criteria decision-making method, named AHP (Analytic Hierarchy Process), is introduced to transform both qualitative personal preferences and users’ priorities into numeric weights. Furthermore, a QoS-aware service evaluation method is presented for a shared service’s co-selection taking advantage of AHP theory. At last, a case study is presented to demonstrate the feasibility of the method.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2013
Publisher: ACM
Date: 17-10-2022
Publisher: Wiley
Date: 23-02-2021
DOI: 10.1002/CPE.6233
Abstract: Recommender systems are important applications in big data analytics because accurate recommendation items or high‐valued suggestions can bring high profit to both commercial companies and customers. To make precise recommendations, a recommender system often needs large and fine‐grained data for training. In the current big data era, data often exists in the form of isolated islands, and it is difficult to integrate the data scattered due to privacy security concerns. Moreover, privacy laws and regulations make it harder to share data. Therefore, designing a privacy‐preserving recommender system is of paramount importance. Existing privacy‐preserving recommender system models mainly adapt cryptography approaches to achieve privacy preservation. However, cryptography approaches have heavy overhead when performing encryption and decryption operations and they lack a good level of flexibility. In this paper, we conduct privacy analysis on the existing locality sensitive hashing (LSH) approach based privacy‐preserving recommender system and show how an attacker can retrieve user's information under such a recommender system. Given such privacy risks, we propose differentially private LSH approach to build recommender system that can offer differential privacy guarantees for users. Our proposed efficient and scalable federated recommender system can make full use of multiple source data from different data owners while guaranteeing privacy preservation of users' data in contributing parties. Extensive experiments on real‐world benchmark datasets show that our approach can achieve both high time efficiency and accuracy under small privacy budgets.
Publisher: IEEE
Date: 07-2019
Publisher: Elsevier BV
Date: 04-2020
Publisher: Springer International Publishing
Date: 2020
Publisher: Association for Computing Machinery (ACM)
Date: 21-06-2021
DOI: 10.1145/3448414
Abstract: Recently, biometric identification has been extensively used for border control. Some face recognition systems have been designed based on Internet of Things. But the rich personal information contained in face images can cause severe privacy breach and abuse issues during the process of identification if a biometric system has compromised by insiders or external security attacks. Encrypting the query face image is the state-of-the-art solution to protect an in idual’s privacy but incurs huge computational cost and poses a big challenge on time-critical identification applications. However, due to their high computational complexity, existing methods fail to handle large-scale biometric repositories where a target face is searched. In this article, we propose an efficient privacy-preserving face recognition scheme based on clustering. Concretely, our approach innovatively matches an encrypted face query against clustered faces in the repository to save computational cost while guaranteeing identification accuracy via a novel multi-matching scheme. To the best of our knowledge, our scheme is the first to reduce the computational complexity from O(M) in existing methods to approximate O (√ M ), where M is the size of a face repository. Extensive experiments on real-world datasets have shown the effectiveness and efficiency of our scheme.
Publisher: Springer Science and Business Media LLC
Date: 09-2023
Publisher: Wiley
Date: 04-07-2013
DOI: 10.1002/CPE.3083
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2019
Publisher: ACM
Date: 17-10-2022
Publisher: Springer International Publishing
Date: 2020
Publisher: Springer International Publishing
Date: 2016
Publisher: Springer International Publishing
Date: 2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2016
Publisher: Elsevier BV
Date: 05-2021
Publisher: ACM
Date: 11-02-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2021
Publisher: IEEE
Date: 10-2020
Publisher: IEEE
Date: 10-2020
Publisher: IEEE
Date: 04-2017
Publisher: Elsevier BV
Date: 12-2014
Publisher: Springer Nature Switzerland
Date: 2022
Publisher: Springer International Publishing
Date: 2016
Publisher: IEEE
Date: 08-2018
Publisher: ACM
Date: 06-10-2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2023
Publisher: IEEE
Date: 11-2015
DOI: 10.1109/CCBD.2015.66
Publisher: Springer New York
Date: 2015
Publisher: IEEE
Date: 11-2022
Publisher: IEEE
Date: 12-2022
DOI: 10.1109/SMARTWORLD-UIC-ATC-SCALCOM-DIGITALTWIN-PRICOMP-METAVERSE56740.2022.00041
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2022
Publisher: IEEE
Date: 09-2021
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2020
Publisher: IEEE
Date: 07-2022
Publisher: Wiley
Date: 29-08-2018
DOI: 10.1002/CPE.4884
Publisher: IEEE
Date: 07-2013
Publisher: Wiley
Date: 08-04-2023
DOI: 10.1002/SPE.3206
Publisher: Hindawi Limited
Date: 2016
DOI: 10.1155/2016/4397061
Abstract: With the advent of “Internet of Everything” (IoE) age, an excessive number of IoE services are emerging on the web, which places a heavy burden on the service selection decision of target users. In this situation, various recommendation techniques are introduced to alleviate the burden, for ex le, Collaborative Filtering- (CF-) based recommendation. Generally, CF-based recommendation approaches utilize similar friends or similar services to achieve the recommendation goal. However, due to the sparsity of user feedback, it is possible that a target user has no similar friends and similar services in this situation, traditional CF-based approaches fail to produce a satisfying recommendation result. Besides, recommendation accuracy would be decreased if time factor is overlooked, as IoE service quality often varies with time. In view of these challenges, a time-aware service recommendation approach named S e r _ R e c t i m e is proposed in this paper. Concretely, we first calculate the time-aware user similarity afterwards, indirect friends of the target user are inferred by Social Balance Theory (e.g., “enemy’s enemy is a friend” rule) finally, the services preferred by indirect friends of the target user are recommended to the target user. At last, through a set of experiments deployed on dataset WS-DREAM, we validate the feasibility of our proposal.
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: International Joint Conferences on Artificial Intelligence Organization
Date: 07-2022
Abstract: Recently issued data privacy regulations like GDPR (General Data Protection Regulation) grant in iduals the right to be forgotten. In the context of machine learning, this requires a model to forget about a training data s le if requested by the data owner (i.e., machine unlearning). As an essential step prior to machine unlearning, it is still a challenge for a data owner to tell whether or not her data have been used by an unauthorized party to train a machine learning model. Membership inference is a recently emerging technique to identify whether a data s le was used to train a target model, and seems to be a promising solution to this challenge. However, straightforward adoption of existing membership inference approaches fails to address the challenge effectively due to being originally designed for attacking membership privacy and suffering from several severe limitations such as low inference accuracy on well-generalized models. In this paper, we propose a novel membership inference approach inspired by the backdoor technology to address the said challenge. Specifically, our approach of Membership Inference via Backdooring (MIB) leverages the key observation that a backdoored model behaves very differently from a clean model when predicting on deliberately marked s les created by a data owner. Appealingly, MIB requires data owners' marking a small number of s les for membership inference and only black-box access to the target model, with theoretical guarantees for inference results. We perform extensive experiments on various datasets and deep neural network architectures, and the results validate the efficacy of our approach, e.g., marking only 0.1% of the training dataset is practically sufficient for effective membership inference.
Publisher: MDPI AG
Date: 26-06-2018
DOI: 10.3390/S18072037
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: Springer International Publishing
Date: 2015
Publisher: Elsevier BV
Date: 08-2015
Publisher: Springer Science and Business Media LLC
Date: 11-12-2017
Publisher: IEEE
Date: 08-2022
DOI: 10.1109/ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS55523.2022.00043
Publisher: Springer International Publishing
Date: 2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Elsevier BV
Date: 05-2022
Publisher: Elsevier BV
Date: 09-2020
Publisher: Elsevier BV
Date: 02-2020
Publisher: Springer International Publishing
Date: 2019
Publisher: IEEE
Date: 13-10-2022
No related grants have been discovered for Xuyun Zhang.