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Data protection and privacy is becoming increasingly crucial in the digital era. Numerous companies depend on third-party vendors and service providers to carry out critical functions within their operations, encompassing tasks such as data handling and storage. However, this reliance introduces potential vulnerabilities, as these vendors' security measures and practices may not always align with the standards expected by regulatory bodies. Businesses are required, often under the penalty of law, to ensure compliance with the evolving regulatory rules. Interpreting and implementing these regulations pose challenges due to their complexity. Regulatory documents are extensive, demanding significant effort for interpretation, while vendor-drafted privacy policies often lack the detail required for full legal compliance, leading to ambiguity. To ensure a concise interpretation of the regulatory requirements and compliance of organizational privacy policy with said regulations, we propose a Large Language Model (LLM) and Semantic Web based approach for privacy compliance. In this paper, we develop the novel Privacy Policy Compliance Verification Knowledge Graph, PrivComp-KG. It is designed to efficiently store and retrieve comprehensive information concerning privacy policies, regulatory frameworks, and domain-specific knowledge pertaining to the legal landscape of privacy. Using Retrieval Augmented Generation, we identify the relevant sections in a privacy policy with corresponding regulatory rules. This information about individual privacy policies is populated into the PrivComp-KG. Combining this with the domain context and rules, the PrivComp-KG can be queried to check for compliance with privacy policies by each vendor against relevant policy regulations. We demonstrate the relevance of the PrivComp-KG, by verifying compliance of privacy policy documents for various organizations.

It is challenging to balance the privacy and accuracy for federated query processing over multiple private data silos. In this work, we will demonstrate an end-to-end workflow for automating an emerging privacy-preserving technique that uses a deep learning model trained using the Differentially-Private Stochastic Gradient Descent (DP-SGD) algorithm to replace portions of actual data to answer a query. Our proposed novel declarative privacy-preserving workflow allows users to specify "what private information to protect" rather than "how to protect". Under the hood, the system automatically chooses query-model transformation plans as well as hyper-parameters. At the same time, the proposed workflow also allows human experts to review and tune the selected privacy-preserving mechanism for audit/compliance, and optimization purposes.

Big Data empowers the farming community with the information needed to optimize resource usage, increase productivity, and enhance the sustainability of agricultural practices. The use of Big Data in farming requires the collection and analysis of data from various sources such as sensors, satellites, and farmer surveys. While Big Data can provide the farming community with valuable insights and improve efficiency, there is significant concern regarding the security of this data as well as the privacy of the participants. Privacy regulations, such as the EU GDPR, the EU Code of Conduct on agricultural data sharing by contractual agreement, and the proposed EU AI law, have been created to address the issue of data privacy and provide specific guidelines on when and how data can be shared between organizations. To make confidential agricultural data widely available for Big Data analysis without violating the privacy of the data subjects, we consider privacy-preserving methods of data sharing in agriculture. Deep learning-based synthetic data generation has been proposed for privacy-preserving data sharing. However, there is a lack of compliance with documented data privacy policies in such privacy-preserving efforts. In this study, we propose a novel framework for enforcing privacy policy rules in privacy-preserving data generation algorithms. We explore several available agricultural codes of conduct, extract knowledge related to the privacy constraints in data, and use the extracted knowledge to define privacy bounds in a privacy-preserving generative model. We use our framework to generate synthetic agricultural data and present experimental results that demonstrate the utility of the synthetic dataset in downstream tasks. We also show that our framework can evade potential threats and secure data based on applicable regulatory policy rules.

Access control is a critical component of computer security, governing access to system resources. However, designing policies and roles in traditional access control can be challenging and difficult to maintain in dynamic and complex systems, which is particularly problematic for organizations with numerous resources. Furthermore, traditional methods suffer from issues such as third-party involvement, inefficiency, and privacy gaps, making transparent and dynamic access control an ongoing research problem. Moreover detecting malicious activities and identifying users who are not behaving appropriately can present notable difficulties. To address these challenges, we propose DLACB, a Deep Learning Based Access Control Using Blockchain, as a solution to decentralized access control. DLACB uses blockchain to provide transparency, traceability, and reliability in various domains such as medicine, finance, and government while taking advantage of deep learning to not rely on predefined policies and eventually automate access control. With the integration of blockchain and deep learning for access control, DLACB can provide a general framework applicable to various domains, enabling transparent and reliable logging of all transactions. As all data is recorded on the blockchain, we have the capability to identify malicious activities. We store a list of malicious activities in the storage system and employ a verification algorithm to cross-reference it with the blockchain. We conduct measurements and comparisons of the smart contract processing time for the deployed access control system in contrast to traditional access control methods, determining the time overhead involved. The processing time of DLBAC demonstrates remarkable stability when exposed to increased request volumes., Comment: arXiv admin note: text overlap with arXiv:2303.14758

In the present era of advanced technology, the Internet of Things (IoT) plays a crucial role in enabling smart connected environments. This includes various domains such as smart homes, smart healthcare, smart cities, smart vehicles, and many others.With ubiquitous smart connected devices and systems, a large amount of data associated with them is at a prime risk from malicious entities (e.g., users, devices, applications) in these systems. Innovative technologies, including cloud computing, Machine Learning (ML), and data analytics, support the development of anomaly detection models for the Vehicular Internet of Things (V-IoT), which encompasses collaborative automatic driving and enhanced transportation systems. However, traditional centralized anomaly detection models fail to provide better services for connected vehicles due to issues such as high latency, privacy leakage, performance overhead, and model drift. Recently, Federated Learning (FL) has gained significant recognition for its ability to address data privacy concerns in the IoT domain. Digital Twin (DT), proves beneficial in addressing uncertain crises and data security issues by creating a virtual replica that simulates various factors, including traffic trajectories, city policies, and vehicle utilization. However, the effectiveness of a V-IoT DT system heavily relies on the collection of long-term and high-quality data to make appropriate decisions. This paper introduces a Hierarchical Federated Learning (HFL) based anomaly detection model for V-IoT, aiming to enhance the accuracy of the model. Our proposed model integrates both DT and HFL approaches to create a comprehensive system for detecting malicious activities using an anomaly detection model. Additionally, real-world V-IoT use case scenarios are presented to demonstrate the application of the proposed model.

In general, deep learning models use to make informed decisions immensely. Developed models are mainly based on centralized servers, which face several issues, including transparency, traceability, reliability, security, and privacy. In this research, we identify a research gap in a distributed nature-based access control that can solve those issues. The innovative technology blockchain could fill this gap and provide a robust solution. Blockchain's immutable and distributed nature designs a useful framework in various domains such as medicine, finance, and government, which can also provide access control as opposed to centralized methods that rely on trusted third parties to access the resources. In existing frameworks, a traditional access control approach is developed using blockchain, which depends on predefined policies and permissions that are not reliable. In this research, we propose DLACB: Deep Learning Based Access Control Using Blockchain, which utilizes a deep learning access control mechanism to determine a user's permissions on a given resource. This proposed framework authenticates the users and logs the access requests on the blockchain to recognize malicious users. The results show that this proposed framework operates correctly for all possible scenarios.

With the ubiquitous advancement in smart medical devices and systems, the potential of Remote Patient Monitoring (RPM) network is evolving in modern healthcare systems. The medical professionals (doctors, nurses, or medical experts) can access vitals and sensitive physiological information about the patients and provide proper treatment to improve the quality of life through the RPM network. However, the wireless nature of communication in the RPM network makes it challenging to design an efficient mechanism for secure communication. Many authentication schemes have been proposed in recent years to ensure the security of the RPM network. Pseudonym, digital signature, and Authenticated Key Exchange (AKE) protocols are used for the Internet of Medical Things (IoMT) to develop secure authorization and privacy-preserving communication. However, traditional authentication protocols face overhead challenges due to maintaining a large set of key-pairs or pseudonyms results on the hospital cloud server. In this research work, we identify this research gap and propose a novel secure and efficient privacy-preserving authentication scheme using cuckoo filters for the RPM network. The use of cuckoo filters in our proposed scheme provides an efficient way for mutual anonymous authentication and a secret shared key establishment process between medical professionals and patients. Moreover, we identify the misbehaving sensor nodes using a correlation-based anomaly detection model to establish secure communication. The security analysis and formal security validation using SPAN and AVISPA tools show the robustness of our proposed scheme against message modification attacks, replay attacks, and man-in-the-middle attacks.

Internet of Medical Things (IoMT) is becoming ubiquitous with a proliferation of smart medical devices and applications used in smart hospitals, smart-home based care, and nursing homes. It utilizes smart medical devices and cloud computing services along with core Internet of Things (IoT) technologies to sense patients' vital body parameters, monitor health conditions and generate multivariate data to support just-in-time health services. Mostly, this large amount of data is analyzed in centralized servers. Anomaly Detection (AD) in a centralized healthcare ecosystem is often plagued by significant delays in response time with high performance overhead. Moreover, there are inherent privacy issues associated with sending patients' personal health data to a centralized server, which may also introduce several security threats to the AD model, such as possibility of data poisoning. To overcome these issues with centralized AD models, here we propose a Federated Learning (FL) based AD model which utilizes edge cloudlets to run AD models locally without sharing patients' data. Since existing FL approaches perform aggregation on a single server which restricts the scope of FL, in this paper, we introduce a hierarchical FL that allows aggregation at different levels enabling multi-party collaboration. We introduce a novel disease-based grouping mechanism where different AD models are grouped based on specific types of diseases. Furthermore, we develop a new Federated Time Distributed (FedTimeDis) Long Short-Term Memory (LSTM) approach to train the AD model. We present a Remote Patient Monitoring (RPM) use case to demonstrate our model, and illustrate a proof-of-concept implementation using Digital Twin (DT) and edge cloudlets.

The growth in Remote Patient Monitoring (RPM) services using wearable and non-wearable Internet of Medical Things (IoMT) promises to improve the quality of diagnosis and facilitate timely treatment for a gamut of medical conditions. At the same time, the proliferation of IoMT devices increases the potential for malicious activities that can lead to catastrophic results including theft of personal information, data breach, and compromised medical devices, putting human lives at risk. IoMT devices generate tremendous amount of data that reflect user behavior patterns including both personal and day-to-day social activities along with daily routine health monitoring. In this context, there are possibilities of anomalies generated due to various reasons including unexpected user behavior, faulty sensor, or abnormal values from malicious/compromised devices. To address this problem, there is an imminent need to develop a framework for securing the smart health care infrastructure to identify and mitigate anomalies. In this paper, we present an anomaly detection model for RPM utilizing IoMT and smart home devices. We propose Hidden Markov Model (HMM) based anomaly detection that analyzes normal user behavior in the context of RPM comprising both smart home and smart health devices, and identifies anomalous user behavior. We design a testbed with multiple IoMT devices and home sensors to collect data and use the HMM model to train using network and user behavioral data. Proposed HMM based anomaly detection model achieved over 98% accuracy in identifying the anomalies in the context of RPM.

The exponential growth of Internet of Things (IoT) has become a transcending force in creating innovative smart devices and connected domains including smart homes, healthcare, transportation and manufacturing. With billions of IoT devices, there is a huge amount of data continuously being generated, transmitted, and stored at various points in the IoT architecture. Deep learning is widely being used in IoT applications to extract useful insights from IoT data. However, IoT users have security and privacy concerns and prefer not to share their personal data with third party applications or stakeholders. In order to address user privacy concerns, Collaborative Deep Learning (CDL) has been largely employed in data-driven applications which enables multiple IoT devices to train their models locally on edge gateways. In this chapter, we first discuss different types of deep learning approaches and how these approaches can be employed in the IoT domain. We present a privacy-preserving collaborative deep learning approach for IoT devices which can achieve benefits from other devices in the system. This learning approach is analyzed from the behavioral perspective of mobile edge devices using a game-theoretic model. We analyze the Nash Equilibrium in N-player static game model. We further present a novel fair collaboration strategy among edge IoT devices using cluster based approach to solve the CDL game, which enforces mobile edge devices for cooperation. We also present implementation details and evaluation analysis in a real-world smart home deployment., Comment: arXiv admin note: text overlap with arXiv:2007.15215

Cyber-Physical System (CPS) has made a tremendous progress in recent years and also disrupted many technical fields such as smart industries, smart health, smart transportation etc. to flourish the nations economy. However, CPS Security is still one of the concerns for wide adoption owing to high number of devices connecting to the internet and the traditional security solutions may not be suitable to protect the advanced, application specific attacks. This paper presents a programmable device network layer architecture to combat attacks and efficient network monitoring in heterogeneous environment CPS applications. We leverage Industrial control systems (ICS) to discuss the existing issues, highlighting the importance of advanced network layer for CPS. The programmable data plane language (P4) is introduced to detect well known HELLO Flood attack with minimal efforts in the network level and also used to featuring the potential solutions for security.

The application of Internet of Things (IoT) and Machine Learning (ML) to the agricultural industry has enabled the development and creation of smart farms and precision agriculture. The growth in the number of smart farms and potential cooperation between these farms has given rise to the Cooperative Smart Farming (CSF) where different connected farms collaborate with each other and share data for their mutual benefit. This data sharing through CSF has various advantages where individual data from separate farms can be aggregated by ML models and be used to produce actionable outputs which then can be utilized by all the farms in CSFs. This enables farms to gain better insights for enhancing desired outputs, such as crop yield, managing water resources and irrigation schedules, as well as better seed applications. However, complications may arise in CSF when some of the farms do not transfer high-quality data and rather rely on other farms to feed ML models. Another possibility is the presence of rogue farms in CSFs that want to snoop on other farms without actually contributing any data. In this paper, we analyze the behavior of farms participating in CSFs using game theory approach, where each farm is motivated to maximize its profit. We first present the problem of defective farms in CSFs due to lack of better data, and then propose a ML framework that segregates farms and automatically assign them to an appropriate CSF cluster based on the quality of data they provide. Our proposed model rewards the farms supplying better data and penalize the ones that do not provide required data or are malicious in nature, thus, ensuring the model integrity and better performance all over while solving the defective farms problem.

With the growth of Internet of Things (IoT) and mo-bile edge computing, billions of smart devices are interconnected to develop applications used in various domains including smart homes, healthcare and smart manufacturing. Deep learning has been extensively utilized in various IoT applications which require huge amount of data for model training. Due to privacy requirements, smart IoT devices do not release data to a remote third party for their use. To overcome this problem, collaborative approach to deep learning, also known as Collaborative DeepLearning (CDL) has been largely employed in data-driven applications. This approach enables multiple edge IoT devices to train their models locally on mobile edge devices. In this paper,we address IoT device training problem in CDL by analyzing the behavior of mobile edge devices using a game-theoretic model,where each mobile edge device aims at maximizing the accuracy of its local model at the same time limiting the overhead of participating in CDL. We analyze the Nash Equilibrium in anN-player static game model. We further present a novel cluster-based fair strategy to approximately solve the CDL game to enforce mobile edge devices for cooperation. Our experimental results and evaluation analysis in a real-world smart home deployment show that 80% mobile edge devices are ready to cooperate in CDL, while 20% of them do not train their local models collaboratively.

Internet of Things (IoT) has grown rapidly in the last decade and continue to develop in terms of dimension and complexity offering wide range of devices to support diverse set of applications. With ubiquitous Internet, connected sensors and actuators, networking and communication technology, and artificial intelligence (AI), smart cyber-physical systems (CPS) provide services rendering assistance to humans in their daily lives. However, the recent outbreak of COVID-19 (also known as coronavirus) pandemic has exposed and highlighted the limitations of current technological deployments to curtail this disease. IoT and smart connected technologies together with data-driven applications can play a crucial role not only in prevention, continuous monitoring, and mitigation of the disease, but also enable prompt enforcement of guidelines, rules and government orders to contain such future outbreaks. In this paper, we envision an IoT-enabled ecosystem for intelligent monitoring, pro-active prevention and control, and mitigation of COVID-19. We propose different architectures, applications and technology systems for various smart infrastructures including E-health, smart home, smart supply chain management, smart locality, and smart city, to develop future connected communities to manage and mitigate similar outbreaks. Furthermore, we present research challenges together with future directions to enable and develop these smart communities and infrastructures to fight and prepare against such outbreaks., Comment: Accepted