C3PO: C loud-based C onfidentiality-preserving C ontinuous Query P r o cessing

2022 ◽  
Vol 25 (1) ◽  
pp. 1-36
Author(s):  
Savvas Savvides ◽  
Seema Kumar ◽  
Julian James Stephen ◽  
Patrick Eugster

With the advent of the Internet of things (IoT), billions of devices are expected to continuously collect and process sensitive data (e.g., location, personal health factors). Due to the limited computational capacity available on IoT devices, the current de facto model for building IoT applications is to send the gathered data to the cloud for computation. While building private cloud infrastructures for handling large amounts of data streams can be expensive, using low-cost public (untrusted) cloud infrastructures for processing continuous queries including sensitive data leads to strong concerns over data confidentiality. This article presents C3PO, a confidentiality-preserving, continuous query processing engine, that leverages the public cloud. The key idea is to intelligently utilize partially homomorphic and property-preserving encryption to perform as many computationally intensive operations as possible—without revealing plaintext—in the untrusted cloud. C3PO provides simple abstractions to the developer to hide the complexities of applying complex cryptographic primitives, reasoning about the performance of such primitives, deciding which computations can be executed in an untrusted tier, and optimizing cloud resource usage. An empirical evaluation with several benchmarks and case studies shows the feasibility of our approach. We consider different classes of IoT devices that differ in their computational and memory resources (from a Raspberry Pi 3 to a very small device with a Cortex-M3 microprocessor) and through the use of optimizations, we demonstrate the feasibility of using partially homomorphic and property-preserving encryption on IoT devices.

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 733-751
Author(s):  
D.M. Sheeba

Internet of Things enables many industries to connect to end customers and provide seamless products and services delivery. Due to easy access to network, availability of devices, penetration of IoT services exponentially Growing. Meanwhile, Ensuring the Data Security and Integrity of devices connected to network is paramount. In this work, we bring the efficient way of implementing Secure Algorithm for low powered devices and enhancing the encryption and decryption process. In addition to the data security, to enhance node integrity with less power, Authenticator and intermediate network manager introduced which will acts as a firewall and manager of data flow. To demonstrate the approach, same is implemented using low cost Arduino Uno, Raspberry Pi boards. Arduino Uno used to demonstrate low powered encryption process using EDIA Algorithm and raspberry pi used as nodal manager to manage the integrity of nodes in a low-powered environment. Data Security and Integrity is ensured by the way of enhanced Algorithm and Integrity through BlockChain and results are provided and discussed. Finally result and future enhancement are explained.


Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 600
Author(s):  
Gianluca Cornetta ◽  
Abdellah Touhafi

Low-cost, high-performance embedded devices are proliferating and a plethora of new platforms are available on the market. Some of them either have embedded GPUs or the possibility to be connected to external Machine Learning (ML) algorithm hardware accelerators. These enhanced hardware features enable new applications in which AI-powered smart objects can effectively and pervasively run in real-time distributed ML algorithms, shifting part of the raw data analysis and processing from cloud or edge to the device itself. In such context, Artificial Intelligence (AI) can be considered as the backbone of the next generation of Internet of the Things (IoT) devices, which will no longer merely be data collectors and forwarders, but really “smart” devices with built-in data wrangling and data analysis features that leverage lightweight machine learning algorithms to make autonomous decisions on the field. This work thoroughly reviews and analyses the most popular ML algorithms, with particular emphasis on those that are more suitable to run on resource-constrained embedded devices. In addition, several machine learning algorithms have been built on top of a custom multi-dimensional array library. The designed framework has been evaluated and its performance stressed on Raspberry Pi III- and IV-embedded computers.


Author(s):  
Muhammad Naveed Aman ◽  
Kee Chaing Chua ◽  
Biplab Sikdar

IoT is the enabling technology for a variety of new exciting services in a wide range of application areas including environmental monitoring, healthcare systems, energy management, transportation, and home and commercial automation. However, the low-cost and straightforward nature of IoT devices producing vast amounts of sensitive data raises many security concerns. Among the cyber threats, hardware-level threats are especially crucial for IoT systems. In particular, IoT devices are not physically protected and can easily be captured by an adversary to launch physical and side-channel attacks. This chapter introduces security protocols for IoT devices based on hardware security primitives called physically unclonable functions (PUFs). The protocols are discussed for the following major security principles: authentication and confidentiality, data provenance, and anonymity. The security analysis shows that security protocols based on hardware security primitives are not only secure against network-level threats but are also resilient against physical and side-channel attacks.


2019 ◽  
Vol 8 (3) ◽  
pp. 2937-2942

Introduction of IoT (Internet of Things) has enjoyed vigorous support from governments and research institutions around the world, and remarkable achievements have been obtained till date. IoT systems collect the voluminous amount of data in real time from hospitals, battlefield and daily living environment which is related to privacy and security of people. So, securing collected sensitive data is one of the major challenges in the development of IoT systems. Authenticating the source of collected data is utmost important because the adversary may act as a source which may lead to a breach in security and privacy of people using the IoT network. IoT devices are resource scarce so lightweight methods for network security and privacy need to develop to achieve future development goals. In this paper, a novel lightweight node to node authentication scheme based on watermark is proposed to solve the contradiction between the security and restricted resources of perception layer. To improve the security, Proposed scheme usage node identity and the number of neighbours as input to generate the watermark and use the watermark to calculate the embedding positions which makes node authentication based on temporal dynamics of sensing network. The generated watermark is embedded in fixed size message digest generated using the variable message as input into a low-cost one-way hashing algorithm LOCHA. The embedded bits of watermark extracted at the receiving node and matched to check the authenticity of the sender node. The security analysis and simulations of the proposed scheme show that it can be a good candidate to ensure the authentication of the resource constraint devices which are integral part of Internet of Things at low cost


Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1799
Author(s):  
Dimitrios Myridakis ◽  
Stefanos Papafotikas ◽  
Konstantinos Kalovrektis ◽  
Athanasios Kakarountas

The rapid development of connected devices and the sensitive data, which they produce, is a major challenge for manufacturers seeking to fully protect their devices from attack. Consumers expect their IoT devices and data to be adequately protected against a wide range of vulnerabilities and exploits. Successful attacks target IoT devices, cause security problems, and pose new challenges. Successful attacks from botnets residing on mastered IoT devices increase significantly in number and the severity of the damage they cause is similar to that of a war. The characteristics of attacks vary widely from attack to attack and from time to time. The warnings about the severity of the attacks indicate that there is a need for solutions to address the attacks from birth. In addition, there is a need to quarantine infected IoT devices, preventing the spread of the virus and thus the formation of the botnet. This work introduces the exploitation of side-channel attack techniques to protect the low-cost smart devices intuitively, and integrates a machine learning-based algorithm for Intrusion Detection, exploiting current supply characteristic dissipation. The results of this work showed successful detection of abnormal behavior of smart IoT devices.


2017 ◽  
Author(s):  
JOSEPH YIU

The increasing need for security in microcontrollers Security has long been a significant challenge in microcontroller applications(MCUs). Traditionally, many microcontroller systems did not have strong security measures against remote attacks as most of them are not connected to the Internet, and many microcontrollers are deemed to be cheap and simple. With the growth of IoT (Internet of Things), security in low cost microcontrollers moved toward the spotlight and the security requirements of these IoT devices are now just as critical as high-end systems due to:


2021 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Claudia Campolo ◽  
Giacomo Genovese ◽  
Antonio Iera ◽  
Antonella Molinaro

Several Internet of Things (IoT) applications are booming which rely on advanced artificial intelligence (AI) and, in particular, machine learning (ML) algorithms to assist the users and make decisions on their behalf in a large variety of contexts, such as smart homes, smart cities, smart factories. Although the traditional approach is to deploy such compute-intensive algorithms into the centralized cloud, the recent proliferation of low-cost, AI-powered microcontrollers and consumer devices paves the way for having the intelligence pervasively spread along the cloud-to-things continuum. The take off of such a promising vision may be hurdled by the resource constraints of IoT devices and by the heterogeneity of (mostly proprietary) AI-embedded software and hardware platforms. In this paper, we propose a solution for the AI distributed deployment at the deep edge, which lays its foundation in the IoT virtualization concept. We design a virtualization layer hosted at the network edge that is in charge of the semantic description of AI-embedded IoT devices, and, hence, it can expose as well as augment their cognitive capabilities in order to feed intelligent IoT applications. The proposal has been mainly devised with the twofold aim of (i) relieving the pressure on constrained devices that are solicited by multiple parties interested in accessing their generated data and inference, and (ii) and targeting interoperability among AI-powered platforms. A Proof-of-Concept (PoC) is provided to showcase the viability and advantages of the proposed solution.


2021 ◽  
Vol 11 (15) ◽  
pp. 7169
Author(s):  
Mohamed Allouche ◽  
Tarek Frikha ◽  
Mihai Mitrea ◽  
Gérard Memmi ◽  
Faten Chaabane

To bridge the current gap between the Blockchain expectancies and their intensive computation constraints, the present paper advances a lightweight processing solution, based on a load-balancing architecture, compatible with the lightweight/embedding processing paradigms. In this way, the execution of complex operations is securely delegated to an off-chain general-purpose computing machine while the intimate Blockchain operations are kept on-chain. The illustrations correspond to an on-chain Tezos configuration and to a multiprocessor ARM embedded platform (integrated into a Raspberry Pi). The performances are assessed in terms of security, execution time, and CPU consumption when achieving a visual document fingerprint task. It is thus demonstrated that the advanced solution makes it possible for a computing intensive application to be deployed under severely constrained computation and memory resources, as set by a Raspberry Pi 3. The experimental results show that up to nine Tezos nodes can be deployed on a single Raspberry Pi 3 and that the limitation is not derived from the memory but from the computation resources. The execution time with a limited number of fingerprints is 40% higher than using a classical PC solution (value computed with 95% relative error lower than 5%).


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