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With the deployment of more and more 5g networks, the limitations of 5g networks have been found, which undoubtedly promotes the exploratory research of 6G networks as the next generation solutions. These investigations include the fundamental security and privacy problems associated with 6G technologies. Therefore, in order to consolidate and solidify this foundational research as a basis for future investigations, we have prepared a survey on the status quo of 6G security and privacy. The survey begins with a historical review of previous networking technologies and how they have informed the current trends in 6G networking. We then discuss 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. The survey concludes with a report on the potential use of 6G. Some of the references used in this paper along and further details of several points raised can be found at: security-privacyin5g-6g.github.io.

Searching on encrypted data has become a very important technique in cloud computing. Such searches enable the data owner to search on the encrypted data stored on the cloud without leaking any information. To obtain a better search experience, researchers have proposed many schemes which mainly focus on conjunctive and disjunctive keyword searches. However, conjunction of all the keywords may result in very few results, whereas a disjunction will return too many results. With the current schemes, customizing the relevancy of the keywords to obtain the desired results is difficult. To solve these problems, we propose a novel scheme that supports the search with the user-specified number of keywords contained in the search result. This number n can be used to customize the keyword relevancy. As a result, the data owner could obtain the desired search results containing any n keywords of a keyword set. The proposed scheme also supports the traditional disjunctive and conjunctive keyword searches when n equals 1 or the size of the keyword set, respectively. The keyword could be positive or negative. We first formally define its security and then prove that the proposed scheme is secure against the adaptively chosen keyword attack in the standard model and can defend against the offline keyword guessing attack to some extent. Furthermore, we present a theoretical performance comparison with other schemes as well as the experimental performance evaluations on our implemented scheme.

In recent years, the widespread prevalence of smart devices has created a new class of mobile Internet of Thing applications. Called mobile crowdsensing, these techniques use workers with mobile devices to collect data and send it to task requester for rewards. However, to ensure the optimal allocation of tasks, a centralized server needs to know the precise location of each user, but exposing the workers' exact locations raises privacy concerns. In this paper, we propose a data release mechanism for crowdsensing techniques that satisfies differential privacy, providing rigorous protection of worker locations. The partitioning method is based on worker density and considers non-uniform worker distribution. In addition, we propose a geocast region selection method for task assignment that effectively balances the task assignment success rate with worker travel distances and system overheads. Extensive experiments prove that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

Data aggregation plays an important role in the Internet of Things, and its study and analysis has resulted in a range of innovative services and benefits for people. However, the privacy issues associated with raw sensory data raise significant concerns due to the sensitive nature of the user information it often contains. Thus, numerous schemes have been proposed over the last few decades to preserve the privacy of users’ data. Most methods are based on encryption technology, which is computationally and communicationally expensive. In addition, most methods can only handle a single aggregation function. Therefore, in this paper, we propose a multifunctional data aggregation method with differential privacy. The method is based on machine learning and can support a wide range of statistical aggregation functions, including additive and non-additive aggregation. It 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. And, by only reporting the results of the aggregation to the server, communication efficiency is improved. Extensive experimental results show that the proposed method not only answers flexible aggregation queries that meet diversified aggregation goals, but also produces aggregation results with high accuracy.

This paper surveys the current research status of location privacy issues in mobile applications. The survey spans five aspects of study: the definition of location privacy, attacks and adversaries, mechanisms to preserve the privacy of locations, location privacy metrics, and the current status of location-based applications. Through this comprehensive review, all the interrelated aspects of location privacy are integrated into a unified framework. Additionally, the current research progress in each area is reviewed individually, and the links between existing academic research and its practical applications are identified. 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.

The growing popularity of location-based services is giving untrusted servers relatively free reign to collect huge amounts of location information from mobile users. This information can reveal far more than just a user's locations but other sensitive information, such as the user's interests or daily routines, which raises strong privacy concerns. Differential privacy is a well-acknowledged privacy notion that has become an important standard for the preservation of privacy. Unfortunately, existing privacy preservation methods based on differential privacy protect user location privacy at the cost of utility, aspects of which have to be sacrificed to ensure that privacy is maintained. To solve this problem, we present a new privacy framework that includes a semi-trusted third party. Under our privacy framework, both the server and the third party only hold a part of the user's location information. Neither the server nor the third party knows the exact location of the user. In addition, the proposed perturbation method based on the Johnson Lindenstrauss transform satisfies the differential privacy. Two popular point of interest queries, k-NN and Range, are used to evaluate the method on two real-world data sets. Extensive comparisons against two representative differential privacy-based methods show that the proposed method not only provides a strict privacy guarantee but also significantly improves performance.

To fight against the evolving computer viruses, we must constantly inject new virus patches into the computer networks. This paper addresses the patch injection problem, i.e., the problem of developing a patch injection strategy to mitigate the negative impact of virus attacks. As the impact of an attack depends on not only the patch injection strategy but the unknown virus injection strategy, the patch injection problem is very complicated. This paper initiates the study of the patch injection problem by means of security economics and differential game theory. First, based on a novel virus-patch mixed propagation model, we model the original problem as a differential game. Second, we develop a method for finding a candidate for the Nash equilibrium of the game, examine the structure of the candidate, and give some examples of the candidate. Furthermore, we demonstrate through comparative experiments that the candidate is better in terms of the Nash equilibrium solution concept. Therefore, we recommend the patch injection strategy in the candidate. Finally, we examine the effects of some factors on the performance of the recommended patch injection strategy. Overall, these findings undoubtedly have guiding significance to defense against virus infections.

Background. Breast cancer is the most common type of cancer among females with a high mortality rate. It is essential to classify the estrogen receptor based breast cancer subtypes into correct subclasses, so that the right treatments can be applied to lower the mortality rate. Using gene signatures derived from gene interaction networks to classify breast cancers has proven to be more reproducible and can achieve higher classification performance. However, the interactions in the gene interaction network usually contain many false-positive interactions that do not have any biological meanings. Therefore, it is a challenge to incorporate the reliability assessment of interactions when deriving gene signatures from gene interaction networks. How to effectively extract gene signatures from available resources is critical to the success of cancer classification. Methods. We propose a novel method to measure and extract the reliable (biologically true or valid) interactions from gene interaction networks and incorporate the extracted reliable gene interactions into our proposed RRHGE algorithm to identify significant gene signatures from microarray gene expression data for classifying ER+ and ER− breast cancer samples. Results. The evaluation on real breast cancer samples showed that our RRHGE algorithm achieved higher classification accuracy than the existing approaches.

Entropy is a basic and important concept in information theory. It is also often used as a measure of the unpredictability of a cryptographic key in cryptography research areas. Ubiquitous computing (Ubi-comp) has emerged rapidly as an exciting new paradigm. In this special issue, we mainly selected and discussed papers related with ore theories based on the graph theory to solve computational problems on cryptography and security, practical technologies; applications and services for Ubi-comp including secure encryption techniques, identity and authentication; credential cloning attacks and countermeasures; switching generator with resistance against the algebraic and side channel attacks; entropy-based network anomaly detection; applied cryptography using chaos function, information hiding and watermark, secret sharing, message authentication, detection and modeling of cyber attacks with Petri Nets, and quantum flows for secret key distribution, etc.

Image sharing on online social networks (OSNs) has become an indispensable part of daily social activities, but it has also increased the risk of privacy invasion. An online image can reveal various types of sensitive information, prompting the public to rethink individual privacy needs in OSN image sharing critically. However, the interaction of images and OSN makes the privacy issues significantly complicated. The current real-world solutions for privacy management fail to provide adequate personalized, accurate, and flexible privacy protection. Constructing a more intelligent environment for privacy-friendly OSN image sharing is urgent in the near future. Meanwhile, given the dynamics in both users' privacy needs and OSN context, a comprehensive understanding of OSN image privacy throughout the entire sharing process is preferable to any views from a single side, dimension, or level. To fill this gap, we contribute a survey of "privacy intelligence" that targets modern privacy issues in dynamic OSN image sharing from a user-centric perspective. Specifically, we present the important properties and a taxonomy of OSN image privacy, along with a highlevel privacy analysis framework based on the lifecycle of OSN image sharing. The framework consists of three stages with different principles of privacy by design. At each stage, we identify typical user behaviors in OSN image sharing and their associated privacy issues. Then a systematic review of representative intelligent solutions to those privacy issues is conducted, also in a stage-based manner. The analysis results in an intelligent "privacy firewall" for closed-loop privacy management. Challenges and future directions in this area are also discussed. [ABSTRACT FROM AUTHOR]

The outstanding performance of deep neural networks has promoted deep learning applications in a broad set of domains. However, the potential risks caused by adversarial samples have hindered the large-scale deployment of deep learning. In these scenarios, adversarial perturbations, imperceptible to human eyes, significantly decrease the model's final performance. Many papers have been published on adversarial attacks and their countermeasures in the realm of deep learning. Most focus on evasion attacks, where the adversarial examples are found at test time, as opposed to poisoning attacks where poisoned data is inserted into the training data. Further, it is difficult to evaluate the real threat of adversarial attacks or the robustness of a deep learning model, as there are no standard evaluation methods. Hence, with this article, we review the literature to date. Additionally, we attempt to offer the first analysis framework for a systematic understanding of adversarial attacks. The framework is built from the perspective of cybersecurity to provide a lifecycle for adversarial attacks and defenses. [ABSTRACT FROM AUTHOR]

With the rapid development of social networks, studying and analyzing their structures and behaviors has become one of the most important requirements of businesses. Social network analysis can be used for many different purposes such as product ads, market orientation detection, influential members detection, predicting user behaviors, recommender systems improvements, etc. One of the newest research topics in social network analysis is the enhancement of the information propagation performance in different aspects based on application. In this paper, a new method is proposed to improve few metrics such as distribution time and precision on social networks. In this method, the local attributes of nodes and also the structural information of the network is used to forward data across the network and reduce the propagation time. First of all, the centrality and Assortativity are calculated for all nodes separately to select two sets of nodes with the highest values for both criteria. Then, the initial active nodes of the network are selected by calculating the intersection of the two sets. Next, the distribution paths are detected based on the initial active nodes to calculate the propagation time. The performance analysis results show that the proposed method has better outcomes in comparison to other state-of-the-art methods in terms of distribution time, precision, recall, and AUPR criteria. [ABSTRACT FROM AUTHOR]

The vast majority of artificial intelligence solutions are founded on game theory, and differential privacy is emerging as perhaps the most rigorous and widely adopted privacy paradigm in the field. However, alongside all the advancements made in both these fields, there is not a single application that is not still vulnerable to privacy violations, security breaches, or manipulation by adversaries. Our understanding of the interactions between differential privacy and game theoretic solutions is limited. Hence, we undertook a comprehensive review of literature in the field, finding that differential privacy has several advantageous properties that can make more of a contribution to game theory than just privacy protection. It can also be used to build heuristic models for game-theoretic solutions, to avert strategic manipulations, and to quantify the cost of privacy protection. With a focus on mechanism design, the aim of this article is to provide a new perspective on the currently held impossibilities in game theory, potential avenues to circumvent those impossibilities, and opportunities to improve the performance of game-theoretic solutions with differentially private techniques. [ABSTRACT FROM AUTHOR]

We are embracing an era of Internet of Things (IoT). The latency brought by unstable wireless networks caused by limited resources of IoT devices seriously impacts the quality of services of users, particularly the service delay they experienced. Mobile Edge Computing (MEC) technology provides promising solutions to delay-sensitive IoT applications, where cloudlets (edge servers) are co-located with wireless access points in the proximity of IoT devices. The service response latency for IoT applications can be significantly shortened due to that their data processing can be performed in a local MEC network. Meanwhile, most IoT applications usually impose Service Function Chain (SFC) enforcement on their data transmission, where each data packet from its source gateway of an IoT device to the destination (a cloudlet) of the IoT application must pass through each Virtual Network Function (VNF) in the SFC in an MEC network. However, little attention has been paid on such a service provisioning of multi-source IoT applications in an MEC network with SFC enforcement. In this article, we study service provisioning in an MEC network for multi-source IoT applications with SFC requirements and aiming at minimizing the cost of such service provisioning, where each IoT application has multiple data streams from different sources to be uploaded to a location (cloudlet) in the MEC network for aggregation, processing, and storage purposes. To this end, we first formulate two novel optimization problems: the cost minimization problem of service provisioning for a single multi-source IoT application, and the service provisioning problem for a set of multi-source IoT applications, respectively, and show that both problems are NP-hard. Second, we propose a service provisioning framework in the MEC network for multisource IoT applications that consists of uploading stream data from multiple sources of the IoT application to the MEC network, data stream aggregation and routing through the VNF instance placement and sharing, and workload balancing among cloudlets. Third, we devise an efficient algorithm for the cost minimization problem built upon the proposed service provisioning framework, and further extend the solution for the service provisioning problem of a set of multi-source IoT applications. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are promising. [ABSTRACT FROM AUTHOR]

In multi‐agent reinforcement learning, transfer learning is one of the key techniques used to speed up learning performance through the exchange of knowledge among agents. However, there are three challenges associated with applying this technique to real‐world problems. First, most real‐world domains are partially rather than fully observable. Second, it is difficult to pre‐collect knowledge in unknown domains. Third, negative transfer impedes the learning progress. We observe that differentially private mechanisms can overcome these challenges due to their randomization property. Therefore, we propose a novel differential transfer learning method for multi‐agent reinforcement learning problems, characterized by the following three key features. First, our method allows agents to implement real‐time knowledge transfers between each other in partially observable domains. Second, our method eliminates the constraints on the relevance of transferred knowledge, which expands the knowledge set to a large extent. Third, our method improves robustness to negative transfers by applying differentially exponential noise and relevance weights to transferred knowledge. The proposed method is the first to use the randomization property of differential privacy to stimulate the learning performance in multi‐agent reinforcement learning system. We further implement extensive experiments to demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Online social networks provide an unprecedented opportunity for researchers to analysis various social phenomena. These network data is normally represented as graphs, which contain many sensitive individual information. Publish these graph data will violate users' privacy. Differential privacy is one of the most influential privacy models that provides a rigorous privacy guarantee for data release. However, existing works on graph data publishing cannot provide accurate results when releasing a large number of queries. In this paper, we propose a graph update method transferring the query release problem to an iteration process, in which a large set of queries are used as update criteria. Compared with existing works, the proposed method enhances the accuracy of query results. The extensive experiment proves that the proposed solution outperforms two state-of-the-art methods, the Laplace method and the correlated method, in terms of Mean Absolute Value. It means our methods can retain more utility of the queries while preserving the privacy. [ABSTRACT FROM AUTHOR]

Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called a folksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, tagging recommender systems has been confronted with serious privacy concerns because adversaries may re-identify a user and her/his sensitive information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy-preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Laplace noise to the weight of tags. We present extensive experimental results on two real world datasets, De.licio.us and Bibsonomy. While the personalization algorithm is successful in both cases, our results further suggest the private releasing algorithm can successfully retain the utility of the datasets while preserving users' identity. [ABSTRACT FROM AUTHOR]