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- Security and privacy in 6G networks: New areas and new challenges363
This survey discusses four key aspects of 6G networks – real-time intelligent edge computing, distributed artificial intelligence, intelligent radio, and 3D intercoms – and some promising emerging technologies in each area, along with the relevant security and privacy issues.
- Differentially Private Data Publishing and Analysis: A Survey330
This survey compares the diverse release mechanisms of differentially private data publishing given a variety of input data in terms of query type, the maximum number of queries, efficiency, and accuracy.
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- A blockchain-based location privacy-preserving crowdsensing system170
This paper identifies three ways locations can be disclosed in traditional crowdsensing systems and proposes a novel solution, dubbed a blockchain privacy-preservation crowdsensing system, to address these privacy problems.
- Adversarial Attacks and Defenses in Deep Learning: From a Perspective of Cybersecurity162
The literature to date on adversarial attacks and their countermeasures in the realm of deep learning is reviewed and the first analysis framework for a systematic understanding is offered, built from the perspective of cybersecurity to provide a lifecycle for adversarial attacked and defenses.
- Machine Unlearning: A Survey156
The existing solutions are classified and summarized based on their characteristics within an up-to-date and comprehensive review of each category’s advantages and limitations.
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- Location Privacy and Its Applications: A Systematic Study114
This in-depth analysis of the current state-of-play in location privacy is designed to provide a solid foundation for future studies in the field.
- Privacy and Fairness in Federated Learning: On the Perspective of Tradeoff110
A detailed literature review of privacy and fairness issues is presented, highlighting unique challenges posed by FL and solutions in federated settings and pointing out new research directions in fair and private FL.
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- Correlated Differential Privacy: Feature Selection in Machine Learning95
A correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks is proposed, which can be widely used in machineLearning algorithms, which provide services in industrial areas.
- Differential Privacy and Applications91
This chapter presents three methods that apply differential privacy to achieve location privacy for LBSs: the geo-indistinguishability method, the synthetic differentially private trajectory Publishing method, and the hierarchical location data publishing method, with an emphasis on the last one.
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- Machine Learning Differential Privacy With Multifunctional Aggregation in a Fog Computing Architecture68
This paper proposes a multifunctional data aggregation method with differential privacy that operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server.
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- Privacy Intelligence: A Survey on Image Privacy in Online Social Networks66
A survey of “privacy intelligence” that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective and presents the important properties and a taxonomy ofOSN image privacy, along with a high-level privacy analysis framework based on the lifecycle of OSn image sharing.
- An effective privacy preserving algorithm for neighborhood-based collaborative filtering66
The results from experiments show that the proposed PNCF algorithm can obtain a rigid privacy guarantee without high accuracy loss, and the proposed algorithm can resist a KNN attack while retaining the accuracy of recommendations.
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- Making DeepFakes More Spurious: Evading Deep Face Forgery Detection via Trace Removal Attack60
The experimental results show that the proposed trace removal attack can significantly compromise the detection accuracy of six state-of-the-art DeepFake detectors while causing only a negligible degradation in visual quality.
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- Visual privacy attacks and defenses in deep learning: a survey51
It is shown that deep learning can be used as a powerful privacy attack tool as well as preservation techniques with great potential and insights are shed on incorporating privacy requirements in the deep learning era.
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- A flexible method to defend against computationally resourceful miners in blockchain proof of work48
A general and flexible PoW method that enforces memory usage that blocks computationally resourceful miners and retains the previous design logic without requiring one to replace the original hash function.
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- Silence is Golden: Enhancing Privacy of Location-Based Services by Content Broadcasting and Active Caching in Wireless Vehicular Networks47
A framework that enhances the privacy of LBS by actively caching in the wireless vehicular network scenario by identifying critical parameters that affect privacy, including RSU distance, vehicle speed, query set size of POI, size of each POI entry, and the proportion of channel capacity used for RSU broadcast are proposed.
- Differentially private model publishing in cyber physical systems45
The data publishing problem in cyber physical systems is transferred to a machine learning problem, in which a prediction model will be shared with clients, and the predict model is used to answer current submitted queries and predict results for newly entered queries from the public.
- Prioritized Experience Replay based on Multi-armed Bandit41
This work proposes a dynamic experience replay strategy according to the interaction between the agent and environment, which is called Prioritized Experience Replay based on Multi-armed Bandit (PERMAB), which can adaptively combine multiple priority criteria to measure the importance of the experience.
- A Differentially Private Game Theoretic Approach for Deceiving Cyber Adversaries41
This work proposes a novel differentially private game theoretic approach to cyber deception that can reduce the impacts on network security resulting from changes in the number of systems and resist attacks regardless of attackers’ reasoning power.
- Density-Based Location Preservation for Mobile Crowdsensing With Differential Privacy41
This paper proposes a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations, and proposes a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads.
- Invisible Hand: A Privacy Preserving Mobile Crowd Sensing Framework Based on Economic Models41
A framework that enhances the location privacy of MCS applications by reducing the bidding and assignment steps in the MCS cycle is proposed and economic theory is used to help both the service provider and participants to decide their strategies.
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- PBCert: Privacy-Preserving Blockchain-Based Certificate Status Validation Toward Mass Storage Management39
A privacy-preserving blockchain-based certificate status validation scheme called PBCert, which designs an obscure response to the clients’ certificate status query for the purpose of privacy preserving and is significant in practice.
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- Android malware detection with contrasting permission patterns38
The contrasting permission patterns are introduced to characterize the essential differences between malwares and clean applications from the permission aspect and a framework based on contrasting permission pattern is presented for Android malware detection.
- Graph neural networks: a survey on the links between privacy and security37
A comprehensive review of recent research efforts on security and/or privacy in GNNs is provided and the distinctions and relationships between security and privacy are described, as well as an outlook on future directions of research in this area.
- Artificial intelligence security and privacy: a survey36
This survey systematically categorizing and analyzing the threats and countermeasures in AI systems, which span both the training and inference stages, encompass centralized and distributed settings, and address both conventional and foundation AI models.
- Boosting Model Inversion Attacks With Adversarial Examples35
A new training paradigm for a learning-based model inversion attack that can achieve higher attack accuracy in a black-box setting is proposed and it is shown that the severity of the threat from learning-based model inversion adversaries is underestimated and more robust defenses are required.
- SaaS: A situational awareness and analysis system for massive android malware detection35
This paper proposes a cloud-based malware detection system called SaaS by leveraging and marrying multiple approaches from diverse domains such as natural language processing (n-gram), image processing (GLCM), cryptography, machine learning (random forest) and complex networks.
- New challenges in reinforcement learning: a survey of security and privacy33
This survey presents a comprehensive review to explain and summarize the challenges associated with security and privacy in reinforcement learning from a new perspective, namely that of the Markov Decision Process (MDP).
- Privacy preservation for image data: A GAN‐based method33
This work proposes a model based on a generative adversarial network that protects identity information while preserving face features of the original image as much as possible, and generates a fake image of a face that shares all the same attributes as the originalimage.
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- QUEEN: Query Unlearning Against Model Extraction31
QUEEN (QUEry unlEarNing) is proposed that proactively launches counterattacks on potential model extraction attacks from the very beginning and outperforms the state-of-the-art defenses against various model extraction attacks with a relatively low cost to the model accuracy.
- Differential Advising in Multiagent Reinforcement Learning31
This article is the first to adopt the concept of differential privacy on advising to improve agent learning performance instead of addressing security issues and demonstrates that the proposed method is more efficient in complex environments than the existing methods.
- Privacy-preserving topic model for tagging recommender systems31
This paper proposes a privacy- preserving tag release algorithm, PriTop, designed to satisfy differential privacy, a strict privacy notion with the goal of protecting users in a tagging dataset, and presents extensive experimental results on four real-world datasets.
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- Detecting Suicidal Ideation with Data Protection in Online Communities24
This work proposes a novel data-protecting solution to learn a model to train a local data-preserving model for each user which only shares their own model’s parameters with the server rather than their personal information.
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- Semantic-Preserving Adversarial Text Attacks19
A Bigram and Unigram-based adaptive adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences is devised.
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- Fairness in graph-based semi-supervised learning14
This work aims to explore a more general case where quantities of unlabeled data are provided, indeed leading to a new form of learning paradigm, namely fair semi-supervised learning.
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- SPoFC: A framework for stream data aggregation with local differential privacy5
This paper considers the problem of aggregating the stream data generated from wearable devices in a specific time period in a privacy‐preserving manner and proposes a novel stream data statistical framework, which includes four components, data fitting, skeleton point selection, noisy stream generation, and data aggregation.
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- Query-Efficient Video Adversarial Attack With Stylized Logo on Service Computing3
Sufficient experimental results indicate that SLA can achieve better performance than state-of-the-art methods and still maintain good deception effects when facing various defense methods and serve as a memorandum of possible attack methods for the future.
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- Decentralized Learning with Average Difference Aggregation for Proactive Online Social Care.2
A decentralized learning framework to enable proactive social care equipped with artificial intelligence and data protection, and a novel model aggregation algorithm, namely the average difference descent aggregation (AvgDiffAgg for short).
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- A novel dictionary attack on ECG authentication system using adversarial optimization and clustering1
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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