Android Private Compute Core Google Explains The Important New Privacy Infrastructure – The Internet of Things (IoT) has become a myriad of services and applications that permeate our daily activities. This expansion has brought countless benefits and added value to human society (Khan et al., 2022). IoT-based smart home systems are one of the best examples of future living through IoT integration. Communities around the world are actively adopting this paradigm as a core component of their modernization efforts (Wang et al., 2013). The convergence of wireless sensor network technologies (Hsu et al., 2017) with the IoT heralds a transformative era of global interconnectivity, bringing together many smart devices with cutting-edge functionalities (Katuk et al., 2018; Froiz-MÃguez et al., 2018). . At the core of this technological change is the wireless home automation network, a dynamic system of sensors and actuators that work together, sharing resources and creating connections. This network is a critical technology that will enable the development of competent smart homes.
Furthermore, the dependence on online and wireless communication introduces additional layers of complexity and vulnerability for data exchange and remote control functionality (Xu et al., 2019). These systems rely on a stable and secure connection without downtime, which can be disrupted by network outages or cyber threats. To address these concerns, research is focused on improving the resilience and security of home automation networks (Abu-Tair et al., 2020). This includes developing strong encryption protocols and implementing redundant communication paths to maintain functionality during disruptions. Furthermore, since smart homes generate a large amount of data, advanced data analysis and machine learning algorithms optimize system performance and predict maintenance needs, anticipating potential failures (Nguyen et al., 2021).
Android Private Compute Core Google Explains The Important New Privacy Infrastructure
In a smart home, data from temperature and smoke detection sensors are sent to a central control station for real-time fire detection and electricity and gas consumption to efficiently manage the homes energy, gas and water use. Within these constantly connected homes, a wealth of valuable data is constantly generated by the many smart devices and appliances integrated into the Internet of Things (IoT) ecosystem (Signoretti et al., 2021). IoT devices enable remote control of home appliances, improved energy management and security systems, providing convenience and energy savings (El-Sayed et al., 2017).
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Privacy has become crucial in the rapidly changing IoT ecosystem (Mocrii, Chen & Musilek, 2018; Haney, Furman & Acar, 2020; Edu, Such & Suarez-Tangil, 2020). However, since IoT-based tools and devices are primarily intended to collect data on user behaviors, vital signs, surrounding environments, etc., they are tempting targets for hackers and present numerous security and privacy risks (Geneiatakis et al., 2017). . Because IoT devices have limited power, storage, communication and processing power, traditional security and privacy methods are poorly designed (Haney, Acar & Furman, 2021). Motivated by this, researchers have been forced to develop creative solutions and algorithms to overcome these limitations.
Along with these technological advances, the ethical implications of data privacy and security are gaining attention in smart home environments. The continuous collection and transmission of personal data through IoT devices poses significant risks if not properly protected (Bajaj, Sharma & Singh, 2022). Researchers advocate the adoption of privacy-by-design principles, ensuring that data protection measures are integrated into the development of home automation technologies from the outset (Shouran, Ashari & Priyambodo, 2019). This includes anonymizing data, securing data storage and giving users transparent control over their information. As smart homes become mainstream, addressing these privacy concerns is critical to building trust and ensuring widespread adoption of these technologies.
Cloud computing has enabled many innovative operations (Padhy, Patra & Satapathy, 2011). However, these often do not consider the security and privacy issues that can affect these cloud-enabled services and applications. Due to these difficulties, researchers have been forced to develop new approaches and paradigms that try to provide secure and private mechanisms and services, considering the resource limitations of IoT devices and the open nature of cloud-based services (Krishna et al. al., 2016). In addition, further processing and analysis is required for the data collected in certain applications in order to provide the desired services.
The need for processing power is very high. Thus, to meet the processing and storage needs of users and applications, it is essential to integrate IoT peripherals with cloud service providers. However, there are several difficulties with this type of integration, especially in terms of security and privacy. Cloud service providers have full ownership and control over user data, can track their actions and activities, and identify the IoT devices they use and their types, usage patterns, access times, and recording frequencies. This invasion of privacy has the potential to make users transparent. Therefore, the implementation of procedures and systems that anonymize and protect user data is essential to preserve their security and privacy. Furthermore, due to limited mobility and power sources, IoT devices have limited Internet connectivity, which makes short-range communication protocols such as WiFi, Bluetooth, and ZigBee increasingly popular (Bulgurcu, Cavusoglu & Benbasat, 2010).
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In many scenarios, edge and fog computing has been added as a middle layer between the cloud and IoT layers (Achar, 2022). This strategy is based on the fact that these devices have much more computing, storage and energy capacity than IoT devices. As a result, they can use more sophisticated technologies that allow longer distances, such as LoRaWAN, LTE and LTE-M, and provide long-range communication functions to the Internet (Raghunath & Rengarajan, 2019). These devices provide low-power, short-range wireless communication to other Internet of Things devices. They also typically have more computing and storage power than IoT devices, allowing them to run more resource-intensive programs, such as security and privacy-related algorithms and functions. As a result, edge and cloud computing are reliable entry points to the cloud and directly connected to IoT devices.
Distributed computing and IoT have combined to create shared platforms that provide simultaneous access to data and services. The Internet of Things has enabled the creation of shared platforms, integrating online computing capabilities, giving users instant access to their data and resources from anywhere. When IoT devices are integrated into shared systems, new security risks emerge; therefore, the security of the system design must be guaranteed. Given that multiple users will access and use comparable data and capabilities, this is critical.
For multi-user friendly applications, integrating the IoT into a secure computing environment is critical. A secure IT system is required to host and manage IoT devices and data securely and efficiently. To reduce potential threats to data integrity and privacy, the architecture should incorporate security measures including threat detection, access control, authorization, and authentication. When designing a system that includes multiple users, a secure computing framework must consider users with different rights and abilities. Therefore, an effective and scalable access control system must be implemented to manage user rights and restrict data access to authorized parties while maintaining data integrity and privacy. The large volumes of data produced by Internet of Things devices require a secure computing architecture. Scalable storage solutions that can handle large volumes of data without compromising security and accessibility are essential.
The goal of designing a secure hybrid computing platform with IoT for multi-user systems is general and offers research opportunities in various research areas. The purpose of the proposed study is to develop a secure, scalable, and efficient dynamic architecture for computational offloading. Massive data volumes and multi-user systems must be managed without sacrificing network performance or security. The main goal of the first phase of the research is to design a reliable, scalable and capable computing architecture for processing data from Internet of Things devices. Key design goals include scalability, efficiency, and the ability to handle large amounts of data.
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Additionally, the proposed framework should be flexible to support IoT devices that collect data from multiple sensing sources. Collected data can be protected and kept safe during the integration process. Additionally, the architecture uses cryptographic approaches to ensure the security and privacy of user data. Post-quantum cryptography (PQC) and blockchain technology are combined in a combination cryptosystem. Integrated PQC-blockchain technology uses PQC encryption to protect data from serious threats. This encryption technique helps prevent the risk of sensitive data. The method uses blockchain technology to ensure that immutable and distributed ledgers are used to store sensitive data. Data in the hybrid PQC-blockchain system is analyzed securely and reliably before being integrated into the block.
This secure computing architecture should be developed by integrating the hybrid IoT and PQC-blockchain system, which allows efficient administration of a large number of users with specific access permissions; moreover, by ensuring that data is accessible only to authorized individuals, a robust and scalable access control approach can be implemented, reducing various security threats. The designed distributed systems, which use a hybrid PQC-blockchain system, provide users with instant access to data and services and protect their infrastructure from a wide range of threats.
Following the motivation of our research, we proposed a framework called Trusted IoT Big Data Analytics (TIBDA), which aims to create a scalable, secure and efficient hybrid computing architecture that can handle massive amounts of data in a smart home.
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