Privacy Mechanisms Research Articles

SETUCOM: Secure and Trustworthy Context Management for Context-Aware Security and Privacy in the Internet of Things

IoT technologies facilitate the development and the improvement of pervasive computing by enabling effective context-awareness features. These features enable the IoT applications to detect the user’s situation and adapt their behavior. They also enable context-aware security and privacy, which consist in adapting security and privacy mechanisms’ deployment to the user’s situation. Research studies on context-aware security and privacy focus on security and privacy mechanisms’ implementation but do not consider the secure and trustworthy context management. In this paper, we introduce a new secure and trustworthy context management system for context-aware security and privacy in the smart city: “SETUCOM.” SETUCOM is the implementation of the DTM (Device Trust Management) module of the CASPaaS (Context-Aware Security and Privacy as a Service) architecture. It secures context information exchange by using a lightweight hybrid encryption system adapted to IoT devices and manages trust through artificial intelligence techniques such as Bayesian networks and fuzzy logic. A detailed description of the proposed system is provided, and its main performances are evaluated. The results prove SETUCOM feasibility in context-aware security and privacy for the smart city.

Data-Driven Caching With Users’ Content Preference Privacy in Information-Centric Networks

Information-centric networking (ICN) as an emerging networking paradigm has recently gained significant attention, due to the improvement of content delivery efficiency. The built-in network storage for caching is a key component in ICN to provide low latency service and reduce high backhaul traffic by caching popular content. However, users' content preference contains individual sensitive characteristics which is distinguishable from others. Therefore, in this work, we propose a data-driven caching revenue maximization problem with the considerations of users' local differential privacy. Specifically, we employ dBitFlip, a local differential privacy (LDP) mechanism, to locally add differential private noise to the users' preference content information. We leverage data-driven approach to predict the content popularity based on the reference distribution constructed by the reported noisy preference content data from users, mathematically present the distance between the noisy reference distribution and the true distribution by the tolerance level, and prove the relationship among the tolerance level, differential privacy budget and the confidence level. We provide feasible solutions to the proposed revenue maximization problem, and conduct simulations to show the effectiveness of the proposed scheme.

Is 5G Handover Secure and Private? A Survey

The next-generation mobile cellular communication and networking system (5G) is highly flexible and heterogeneous. It integrates different types of networks, such as 4G legacy networks, Internet of Things (IoT), vehicular ad hoc network (VANET), and wireless local access network (WLAN) to form a heterogeneous network. This easily results in continual vertical handovers between different networks. On the other hand, substantial deployment of small/micro base stations (BSs) brings frequent horizontal handovers within a network. The continual handovers among BSs and various networks expose mobile equipment (ME) to the risk of security and privacy threats. So far, many security and privacy mechanisms have been proposed to ensure secure handover either vertically or horizontally in 5G networks. Nevertheless, there still lacks a thorough survey to summarize recent advances and explore open issues although handover security and privacy are crucial to 5G. In this article, we summarize security and privacy requirements in handovers to resist potential attacks. Following these requirements as the evaluation criteria, we review secure and privacy-preserving handover schemes by categorizing them into two scenarios, i.e., vertical handover and horizontal handover. As for the vertical handover, we review related work from three classes, i.e., handovers within Third-Generation Partnership Project (3GPP) networks, between 3GPP and non-3GPP networks, and between non-3GPP networks. Concerning horizontal handovers, we review related work from two classes, i.e., intramobile service controller (MSC) and inter-MSC handover. Meanwhile, we analyze and compare the technical means and performance of these works in order to uncover open issues and inspire future research directions.

Incorporating individual and group privacy preferences in the internet of things

This paper presents a new privacy negotiation mechanism for an IoT environment that is both efficient and practical to cope with the IoT special need of seamlessness. This mechanism allows IoT users to express and enforce their personal privacy preferences in a seamless manner while interacting with IoT deployments. A key contribution of the paper is that it addresses the privacy concerns of individual users as well as a group of users where privacy preferences of all individual users are combined into a group privacy profile to be negotiated with the IoT owner. In addition, the proposed mechanism satisfies the privacy requirements of the IoT deployment owner. Finally, the proposed privacy mechanism is agnostic to the actual IoT architecture and can be used over a user-managed, edge-managed or a cloud-managed IoT architecture. Prototypes of the proposed mechanism have been implemented for each of these three architectures, and the results show the capability of the protocol to negotiate privacy while adding insignificant time overhead.

5G NR-V2X: Toward Connected and Cooperative Autonomous Driving

5G New Radio (NR) is touted as a pivotal enabling technology for the genuine realization of connected and cooperative autonomous driving. Despite numerous research efforts in recent years, a systematic overview on the role of 5G NR in future connected autonomous communication networks is missing. To fill this gap and to spark more future research, this article introduces the technology components of 5G NR and discusses the evolution from existing cellular vehicle-to-everything (V2X) technology toward NR-V2X. We primarily focus on the key features and functionalities of the physical layer, sidelink communication and its resource allocation, architecture flexibility, security and privacy mechanisms, and precise positioning techniques. Moreover, we envisage and highlight the potential of machine learning for further performance enhancement in NR-V2X services. Lastly, we show how 5G NR can be configured to support advanced V2X use cases.

Trends in East Asian policies on digital surveillance tools during the COVID-19 pandemic

This article investigates how digital surveillance tools used by East Asian governments against COVID-19 affect privacy and personal data protection. It applies doctrinal legal analysis and case study to compare national regulations of these tools as well as their implementation in China, Hong Kong, Macau, Taiwan, Japan and South Korea. The approaches range considerably from total (China) to selective surveillance, which, however, seems overly excessive towards privacy of certain social groups, exacerbating social stratification and business disruptions in East Asia. The article argues that selective surveillance models vary across the region from voluntary selective (Japan) to compulsory selective surveillance (Hong Kong, Macau, Taiwan, South Korea) and differ in terms of privacy and related rights. Yet, the increased risks of data misuse and leakages in all the East Asian states and territories need effective legal mechanisms for privacy and data protection that pay sufficient attention to public scrutiny and independent regulators.

Private Weighted Random Walk Stochastic Gradient Descent

We consider a decentralized learning setting in which data is distributed over nodes in a graph. The goal is to learn a global model on the distributed data without involving any central entity that needs to be trusted. While gossip-based stochastic gradient descent (SGD) can be used to achieve this learning objective, it incurs high communication and computation costs, since it has to wait for all the local models at all the nodes to converge. To speed up the convergence, we propose instead to study random walk based SGD in which a global model is updated based on a random walk on the graph. We propose two algorithms based on two types of random walks that achieve, in a decentralized way, uniform sampling and importance sampling of the data. We provide a non-asymptotic analysis on the rate of convergence, taking into account the constants related to the data and the graph. Our numerical results show that the weighted random walk based algorithm has a better performance for high-variance data. Moreover, we propose a privacy-preserving random walk algorithm that achieves local differential privacy based on a Gamma noise mechanism that we propose. We also give numerical results on the convergence of this algorithm and show that it outperforms additive Laplace-based privacy mechanisms.

Enabling Probabilistic Differential Privacy Protection for Location Recommendations

The sequential pattern in the human movement is one of the most important aspects for location recommendations in geosocial networks. Existing location recommenders have to access users’ raw check-in data to mine their sequential patterns that raises serious location privacy breaches. In this paper, we propose a new P rivacy-preserving LO cation RE commendation framework (PLORE) to address this privacy challenge. First, we employ the $n$ n th-order additive Markov chain to exploit users’ sequential patterns for location recommendations. Further, we contrive the probabilistic differential privacy mechanism to reach a good trade-off between high recommendation accuracy and strict location privacy protection. Finally, we conduct extensive experiments to evaluate the performance of PLORE using three large-scale real-world data sets. Extensive experimental results show that PLORE provides efficient and highly accurate location recommendations, and guarantees strict privacy protection for user check-in data in geosocial networks.

Privacy preservation of electronic health records with adversarial attacks identification in hybrid cloud

An increasing trend in healthcare organizations to outsource EHRs’ data to the cloud highlights new challenges regarding the privacy of given individuals. Healthcare organizations outsource their EHRs data in a hybrid cloud that elevates the problem of security and privacy in terms of EHRs’ access to an unlimited number of recipients in a hybrid cloud environment. In this paper, we investigated the need for a privacy-preserving access control model for the hybrid cloud. A comprehensive and exploratory analysis of privacy-preserving solutions with the help of taxonomy for cloud-based EHRs is described in this work. We have formally identified the existence of internal access control and external privacy disclosures in outsourcing system architecture for hybrid cloud. Then, we proposed a privacy-preserving XACML based access control model (PPX-AC) that supports fine-grained access control with the multipurpose utilization of EHRs alongside state-of-the-art privacy mechanism. Our proposed approach invalidates the identified security and privacy attacks. We have formally verified the proposed privacy-preserving XACML based access control model (PPX-AC) with the invalidation of identified privacy attacks using High-Level Petri Nets (HLPN). Moreover, property verification of the proposed model in SMT-lib and Z3 solver and implementation of the model proves its effectiveness in terms of privacy-aware EHRs access and multipurpose usage.

RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything

While the widespread use of ubiquitously connected devices in Internet of Everything (IoE) offers enormous benefits, it also raises serious privacy concerns. Federated learning, as one of the promising solutions to alleviate such problems, is considered as capable of performing data training without exposing raw data that kept by multiple devices. However, either malicious attackers or untrusted servers can deduce users’ privacy from the local updates of each device. Previous studies mainly focus on privacy-preserving approaches inside the servers, which require the framework to be built on trusted servers. In this article, we propose a privacy-enhanced federated learning scheme for IoE. Two mechanisms are adopted in our approach, namely the randomized response (RR) mechanism and the local adaptive differential privacy (LADP) mechanism. RR is adopted to prevent the server from knowing whose updates are collected in each round. LADP enables devices to add noise adaptively to its local updates before submitting them to the server. Experiments demonstrate the feasibility and effectiveness of our approach.

Fortified-Chain: A Blockchain-Based Framework for Security and Privacy-Assured Internet of Medical Things With Effective Access Control

The rapid developments in the Internet of Medical Things (IoMT) help the smart healthcare systems to deliver more sophisticated real-time services. At the same time, IoMT also raises many privacy and security issues. Also, the heterogeneous nature of these devices makes it challenging to develop a common security standard solution. Furthermore, the existing cloud-centric IoMT healthcare systems depend on cloud computing for electrical health records (EHR) and medical services, which is not suggestible for a decentralized IoMT healthcare systems. In this article, we have proposed a blockchain-based novel architecture that provides a decentralized EHR and smart-contract-based service automation without compromising with the system security and privacy. In this architecture, we have introduced the hybrid computing paradigm with the blockchain-based distributed data storage system to overcome blockchain-based cloud-centric IoMT healthcare system drawbacks, such as high latency, high storage cost, and single point of failure. A decentralized selective ring-based access control mechanism is introduced along with device authentication and patient records anonymity algorithms to improve the proposed system's security capabilities. We have evaluated the latency and cost effectiveness of data sharing on the proposed system using Blockchain. Also, we conducted a logical system analysis, which reveals that our architecture-based security and privacy mechanisms are capable of fulfilling the requirements of decentralized IoMT smart healthcare systems. Experimental analysis proves that our fortified-chain-based H-CPS needs insignificant storage and has a response time in the order of milliseconds as compared to traditional centralized H-CPS while providing decentralized automated access control, security, and privacy.

A Novel and Efficient Priority-Based Cross-Layer Contextual Unobservability Scheme Against Global Attacks for WMSNs

Even though many security schemes proposed for wireless sensor networks protect transmitted data content from being revealed to different types of attacks and fulfill most of the desired security requirements, they are not addressing concealing the privacy of the contextual information. Contextual information such as event occurrence, event time, and event location can be exposed to an adversary by just monitoring network packet transmission. This kind of information is very important because it can leak location information of key nodes or even detected events themselves. Therefore, proposing a contextual unobservability scheme is a challenging task in sensor networks considering many issues: the broadcast nature of the wireless channel, the different attacker models, the network resource constraints, and the overhead on system performance. Most of the existing location privacy schemes are not addressing all these issues and are either not efficient against global adversaries or degrade significantly network performance. Thus in this paper, we propose an efficient location contextual anonymity scheme for Wireless Multimedia Sensor Network (WMSN) that exploits the cross-layer joint design among the application, routing, and MAC layers. Our proposed location unobservability scheme combines the source coding technique, probabilistic packet transmission, multipath routing, and priority-based dropping policy to enhance the efficiency level of the provided privacy service without noticeably affecting the Quality of Service (QoS) requirement for delivering multimedia content in WMSN. Performance evaluation results show that our proposed privacy mechanism outperforms other proposed location privacy techniques in terms of privacy efficiency (safety period) and network performance (end-to-end delay and energy consumption).

Correlated tuple data release via differential privacy

Privacy preserving methods supporting for tuple data release have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP), introducing independent Laplace noise, has become an influential privacy mechanism owing to its provable and rigorous privacy guarantee. Nonetheless, in practice, tuple data to be protected are always correlated while independent noise may cause undesirable information disclosure than expected. Recent researches attempt to optimize the sensitivity function of DP with consideration of the correlation strength between data – but have a drawback in a substantial growth of noise level. Therefore, for correlated tuple data release, how to decrease the noise level incurred by correlation strength is yet to be explored. To remedy this problem, this paper exploits the degradation of DP in expected privacy levels for correlated tuple data and proposes a solution to mitigate it. We first demonstrate a filtering attack, presenting a possibility of using the different dependence between original outputs and perturbations to sanitize a certain level of noise to extract individual’s sensitive information. Secondly, we introduce the notion of correlated tuple differential privacy (CTDP) to preserve expected privacy for correlated tuple data and further propose a generalized Laplace mechanism (GLM) to achieve privacy guarantees in CTDP. Then we design a practical iteration mechanism, including an update function, to conduct GLM when facing large scale queries. Finally, experimental evaluation on real-world datasets over multiple fields show that our solution consistently outperforms state-of-the-art mechanisms in data utility while providing the same privacy guarantee as other approaches for correlated tuple data.

Incentivizing Crowdsensing-Based Noise Monitoring with Differentially-Private Locations

Mobile crowd sensing is a technique where a crowd sensing server outsources sensing tasks to the crowd for mobile data collection. In mobile crowd sensing, some tasks require location information to achieve their objectives, such as road monitoring, indoor floor plan reconstruction, and smart transportation. This required information incurs severe concerns on location privacy leakage and threatens workers' properties as well as public safety. In some cases, even sensing data itself can be used as auxiliary information resulting in location privacy breaches. Many existing works apply differential privacy mechanisms for location privacy preservation to tackle this problem, but they cannot efficiently fulfill privacy goals because each worker only considers his own privacy. As a consequence, the accumulated privacy budget will lower down the composed privacy level of all the workers' locations. In addition, deploying differential privacy is costly for workers and it will degrade the quality of data required in crowd sensing tasks. How to balance the cost and provide accurate aggregated data while fulfilling privacy objectives becomes a challenging issue. In this paper, we propose a group-differentially-private game-theoretical solution, which addresses these limitations in a privacy-preserving and efficient way. Our scheme enables the indistinguishability of workers' locations and sensing data without the help of a trusted entity while meeting the accuracy demands of crowd sensing tasks. The effectiveness and efficiency of our scheme are thoroughly evaluated based on real-world datasets.

A Perfect Match: Converging and Automating Privacy and Security Impact Assessment On-the-Fly

As the upsurge of information and communication technologies has become the foundation of all modern application domains, fueled by the unprecedented amount of data being processed and exchanged, besides security concerns, there are also pressing privacy considerations that come into play. Compounding this issue, there is currently a documented gap between the cybersecurity and privacy risk assessment (RA) avenues, which are treated as distinct management processes and capitalise on rather rigid and make-like approaches. In this paper, we aim to combine the best of both worlds by proposing the APSIA (Automated Privacy and Security Impact Assessment) methodology, which stands for Automated Privacy and Security Impact Assessment. APSIA is powered by the use of interdependency graph models and data processing flows used to create a digital reflection of the cyber-physical environment of an organisation. Along with this model, we present a novel and extensible privacy risk scoring system for quantifying the privacy impact triggered by the identified vulnerabilities of the ICT infrastructure of an organisation. We provide a prototype implementation and demonstrate its applicability and efficacy through a specific case study in the context of a heavily regulated sector (i.e., assistive healthcare domain) where strict security and privacy considerations are not only expected but mandated so as to better showcase the beneficial characteristics of APSIA. Our approach can complement any existing security-based RA tool and provide the means to conduct an enhanced, dynamic and generic assessment as an integral part of an iterative and unified risk assessment process on-the-fly. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that such holistic security and privacy mechanisms can reach their full potential towards solving this conundrum.

A survey on federated learning

Federated learning is a set-up in which multiple clients collaborate to solve machine learning problems, which is under the coordination of a central aggregator. This setting also allows the training data decentralized to ensure the data privacy of each device. Federated learning adheres to two major ideas: local computing and model transmission, which reduces some systematic privacy risks and costs brought by traditional centralized machine learning methods. The original data of the client is stored locally and cannot be exchanged or migrated. With the application of federated learning, each device uses local data for local training, then uploads the model to the server for aggregation, and finally the server sends the model update to the participants to achieve the learning goal. To provide a comprehensive survey and facilitate the potential research of this area, we systematically introduce the existing works of federated learning from five aspects: data partitioning, privacy mechanism, machine learning model, communication architecture and systems heterogeneity. Then, we sort out the current challenges and future research directions of federated learning. Finally, we summarize the characteristics of existing federated learning, and analyze the current practical application of federated learning.

Ensuring security framework for WBANs

Wireless body area(WBANs) network is a new technological development that is capable to monitor and process human health remotely. In this process,the wearable wireless sensor plays a vital role in the collection of information. As the information collected by wearable wireless sensors are very critical security is necessary which can able to take care of security and reliability at the same time. Reliability is necessary to build trust between endusers. Nowadays instead of physical attack new type of cyber attack will take place on the digital information. Due to the new type of attack in the cyber world special type of secure model needed to fulfil the current need. The proposes a secure and efficient authentication and privacy mechanism (SEAPM), which shows a better result in comparison to the existing algorithm in their previous works. This work includes a mechanism which needs less resource comparatively. Only authentic entities can able to interact with the authentic server which essential requisite for both sides.

On a security vs privacy trade-off in interconnected dynamical systems

We study a security problem for interconnected systems, where each subsystem aims to detect local attacks using local measurements and information exchanged with neighboring subsystems. The subsystems also wish to maintain the privacy of their states and, therefore, use privacy mechanisms that share limited or noisy information with other subsystems. We quantify the privacy level based on the estimation error of a subsystem’s state and propose a novel framework to compare different mechanisms based on their privacy guarantees. We develop a local attack detection scheme without assuming the knowledge of the global dynamics, which uses local and shared information to detect attacks with provable guarantees. Additionally, we quantify a trade-off between security and privacy of the local subsystems. Interestingly, we show that, for some instances of the attack, the subsystems can achieve a better detection performance by being more private. We provide an explanation for this counter-intuitive behavior and illustrate our results with examples.

A Design Framework for Strongly χ²-Private Data Disclosure

In this paper, we study a stochastic disclosure control problem using information-theoretic methods. The useful data to be disclosed depend on private data that should be protected. Thus, we design a privacy mechanism to produce new data which maximizes the disclosed information about the useful data under a strong $\chi^2$-privacy criterion. For sufficiently small leakage, the privacy mechanism design problem can be geometrically studied in the space of probability distributions by a local approximation of the mutual information. By using methods from Euclidean information geometry, the original highly challenging optimization problem can be reduced to a problem of finding the principal right-singular vector of a matrix, which characterizes the optimal privacy mechanism. In two extensions we first consider a scenario where an adversary receives a noisy version of the user's message and then we look for a mechanism which finds $U$ based on observing $X$, maximizing the mutual information between $U$ and $Y$ while satisfying the privacy criterion on $U$ and $Z$ under the Markov chain $(Z,Y)-X-U$.

Drone Security: Issues and Challenges

Recent advancements in drone technology are opening new opportunities and applications in various fields of life especially in the form of small drones. However, these advancements are also causing new challenges in terms of security, adaptability, and consistency. This research discusses the drone technology, area of usages, citizen multi-objective uses, drones security, protection, and secrecy apprehensions, drone current intimidations and susceptibilities, existing approaches for drone cyber-security methods, security threats to drones and data sources for current literature review. Small drones are proving to be a new opportunity for the civil and military industries. The small drones are suffering from architectural issues and the definition of security and safety issues. The rapid growth of the Internet of Things (IoT) opens new dimensions for drone technology but posing new threats as well. The tiny flying intelligent devices are challenging for the security and privacy of data. The design of these small drones is yet not matured to fulfill the domain requirements. The basic design issues also need security mechanisms, privacy mechanisms and data transformations.