Securing IoT Devices Generated Data Using Homomorphic Encryption

2020 ◽  
pp. 219-226
Author(s):  
Anita Caudhari ◽  
Rajesh Bansode
Keyword(s):  
Homomorphic Encryption ◽  
Iot Devices

scholarly journals Privacy-Preserving Data Aggregation against False Data Injection Attacks in Fog Computing

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2659 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng ◽  
Kaixin Deng ◽  
Fangyuan Ren ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Cloud Service ◽  
Privacy Preserving ◽  
Location Awareness ◽  
False Data Injection ◽  
Injection Attacks ◽  
Iot Devices

As an extension of cloud computing, fog computing has received more attention in recent years. It can solve problems such as high latency, lack of support for mobility and location awareness in cloud computing. In the Internet of Things (IoT), a series of IoT devices can be connected to the fog nodes that assist a cloud service center to store and process a part of data in advance. Not only can it reduce the pressure of processing data, but also improve the real-time and service quality. However, data processing at fog nodes suffers from many challenging issues, such as false data injection attacks, data modification attacks, and IoT devices’ privacy violation. In this paper, based on the Paillier homomorphic encryption scheme, we use blinding factors to design a privacy-preserving data aggregation scheme in fog computing. No matter whether the fog node and the cloud control center are honest or not, the proposed scheme ensures that the injection data is from legal IoT devices and is not modified and leaked. The proposed scheme also has fault tolerance, which means that the collection of data from other devices will not be affected even if certain fog devices fail to work. In addition, security analysis and performance evaluation indicate the proposed scheme is secure and efficient.

scholarly journals Simulation-Based Performance Validation of Homomorphic Encryption Algorithms in the Internet of Things

2019 ◽  
Vol 11 (10) ◽  
pp. 218 ◽  
Author(s):  
Johannes Kölsch ◽  
Christopher Heinz ◽  
Axel Ratzke ◽  
Christoph Grimm
Keyword(s):  
Data Privacy ◽  
Energy Use ◽  
Homomorphic Encryption ◽  
Real Life ◽  
Value Added ◽  
Physical World ◽  
Software Systems ◽  
Simulation Based ◽  
Iot Devices ◽  
Performance Validation

IoT systems consist of Hardware/Software systems (e.g., sensors) that are embedded in a physical world, networked and that interact with complex software platforms. The validation of such systems is a challenge and currently mostly done by prototypes. This paper presents the virtual environment for simulation, emulation and validation of an IoT platform and its semantic model in real life scenarios. It is based on a decentralized, bottom up approach that offers interoperability of IoT devices and the value-added services they want to use across different domains. The framework is demonstrated by a comprehensive case study. The example consists of the complete IoT “Smart Energy” use case with focus on data privacy by homomorphic encryption. The performance of the network is compared while using partially homomorphic encryption, fully homomorphic encryption and no encryption at all.As a major result, we found that our framework is capable of simulating big IoT networks and the overhead introduced by homomorphic encryption is feasible for VICINITY.

scholarly journals EPPDA: An Efficient and Privacy-Preserving Data Aggregation Scheme with Authentication and Authorization for IoT-Based Healthcare

2021 ◽  
Author(s):  
Faris. A. Almalki ◽  
Ben othman Soufiene
Keyword(s):  
Data Aggregation ◽  
Data Privacy ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Communication Overhead ◽  
Range Data ◽  
Aggregation Techniques ◽  
Aggregation Scheme ◽  
Iot Devices

Abstract Internet of Things (IoT) connects various kinds of intelligent objects and devices using the internet to collect and exchange data. Nowadays, The IoT is used in diverse application domains, including the healthcare. In the healthcare domain, the IoT devices can collects patient data, and its forwards the data to the healthcare professionals can view it. The IoT devices are usually resource-constrained in terms of energy consumption, storage capacity, computational capability, and communication range, data aggregation techniques are used to reduce the communication overhead. However, in healthcare system using IoT, the heterogeneity of technologies, the large number of devices and systems, and the different types of users and roles create important challenges in terms of security. For that, the security and privacy aggregation of health data are very important aspects. In this paper, we propose a novel secure data aggregation scheme based on homomorphic primitives in IoT based healthcare systems, called “An Efficient and Privacy-Preserving Data Aggregation Scheme with authentication for IoT-Based Healthcare applications” (EPPDA). EPPDA is based the Verification and Authorization phase to verifying the legitimacy of the nodes wants to join the process of aggregation. EPPDA uses additive homomorphic encryption to protect data privacy and combines it with homomorphic MAC to check the data integrity. The security analysis and experimental results show that our proposed scheme guarantees data privacy, messages authenticity, and integrity, with lightweight communication overhead and computation.

scholarly journals Compare encryption performance across devices to ensure the security of the IOT

2020 ◽  
Vol 20 (2) ◽  
pp. 894
Author(s):  
A.YU. Pyrkova ◽  
ZH.E. Temirbekova
Keyword(s):  
Homomorphic Encryption ◽  
Security And Privacy ◽  
Symmetric Cryptography ◽  
Fully Homomorphic Encryption ◽  
Cryptographic Primitives ◽  
Development Platform ◽  
Encryption And Decryption ◽  
Detailed Assessment ◽  
Iot Devices ◽  
The Internet Of Things

The Internet of Things (IoT) combines many devices with various platforms, computing capabilities and functions. The heterogeneity of the network and the ubiquity of IoT devices place increased demands on security and privacy protection. Therefore, cryptographic mechanisms must be strong enough to meet these increased requirements, but at the same time they must be effective enough to be implemented on devices with disabilities. One of the limited devices are microcontrollers and smart cards. This paper presents the performance and memory limitations of modern cryptographic primitives and schemes on various types of devices that can be used in IoT. In this article, we provide a detailed assessment of the performance of the most commonly used cryptographic algorithms on devices with disabilities that often appear on IoT networks. We relied on the most popular open source microcontroller development platform, on the mbed platform. To provide a data protection function, we use cryptography asymmetric fully homomorphic encryption in the binary ring and symmetric cryptography AES 128 bit. In addition, we compared run-time encryption and decryption on a personal computer (PC) with Windows 7, the Bluetooth Low Energy (BLE) Nano Kit microcontroller, the BLE Nano 1.5, and the smartcard ML3-36k-R1.

scholarly journals Leveraging Fog Computing for a Secure and Smart Healthcare

2019 ◽  
Vol 8 (2) ◽  
pp. 6117-6122
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Healthcare Services ◽  
Decision Fusion ◽  
Sensor Data ◽  
Smart Sensors ◽  
Healthcare Applications ◽  
Smart Healthcare ◽  
Iot Devices

From hairbrushes to scales, all devices have sensors embedded in them to collect and communicate data. Smart Healthcare is proving to be an exciting and dynamic area with lots of room for new innovations and the increasing consumer demand for proactive health monitoring devices. Having India poised to spend a lot on healthcare, recent innovations using IoT devices and big data analytics can propel the healthcare industry into the future. Smart healthcare providers are leveraging cloud computing with fog computing to optimize their healthcare services. These smart healthcare applications depend mainly on the raw sensor data collected, aggregated, and analyzed by the smart sensors. Smart sensors these days generate myriad amount of data like text, image, audio, and video that require real-time or batch processing. Aggregating these diverse data from various types of resources remains a dispute till date. To resolve this issue, we have proposed a softwarized infrastructure that integrates cloud computing and fog computing, message brokers, and Tor for supple, safe, viable, and a concealed IoT exploitation for smart healthcare applications and services. Our proposed platform employs machine-to-machine (M2M) messaging, data fusion and decision fusion, and uses rule-based beacons for seamless data management. Our proposed flexBeacon system provides an IoT infrastructure that is nimble, secure, flexible, private, and reasonable. We have also proposed an M2M transceiver and microcontroller for flawless data incorporation of smart healthcare applications and services. Based on the IoT devices’ technical capabilities and resource availability, some systems are capable of making use of homomorphic encryption and zero knowledge proofs. The proposed flexBeacon platform offers seamless management and data aggregation without loss of accuracy. The cost of implementing a softwarized IoT for smart healthcare is also greatly reduced.

scholarly journals Privacy-Preserving Internet of Things: Techniques and Applications

2019 ◽  
Vol 8 (6) ◽  
pp. 3229-3234 ◽  
Keyword(s):  
Cloud Computing ◽  
Homomorphic Encryption ◽  
Fog Computing ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Multiparty Computation ◽  
Iot Applications ◽  
Attribute Based Encryption ◽  
Daily Behavior ◽  
Iot Devices

Privacy has become an imperative term in the recent technology developments. Lots of data are being collected through every digital activity of users. The expeditious development of IoT applications have raised the concern about the privacy of the IoT systems. The data collected via IoT sensors can reveal the daily behavior of the users, location, and other sensitive information. Hence, it is necessary to preserve the privacy of data collected by IoT devices. A large number of techniques and approaches have been implemented and used in different IoT based applications such as cloud computing based IoT, fog computing based IoT, blockchain based IoT and trajectory applications. In this paper, we present a detailed investigation of the existing approaches to preserve the privacy of data in IoT applications. The techniques like k-anonymity, secure multiparty computation, attribute based encryption and homomorphic encryption are analyzed. Finally, a comparative analysis of privacy preserving techniques with its applications are presented.

scholarly journals Privacy-Preserving IoT Data Aggregation Based on Blockchain and Homomorphic Encryption

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2452
Author(s):  
Faiza Loukil ◽  
Chirine Ghedira-Guegan ◽  
Khouloud Boukadi ◽  
Aïcha-Nabila Benharkat
Keyword(s):  
Data Aggregation ◽  
Homomorphic Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Aggregation Process ◽  
Smart Contract ◽  
Reasonable Choice ◽  
Iot Devices

Data analytics based on the produced data from the Internet of Things (IoT) devices is expected to improve the individuals’ quality of life. However, ensuring security and privacy in the IoT data aggregation process is a non-trivial task. Generally, the IoT data aggregation process is based on centralized servers. Yet, in the case of distributed approaches, it is difficult to coordinate several untrustworthy parties. Fortunately, the blockchain may provide decentralization while overcoming the trust problem. Consequently, blockchain-based IoT data aggregation may become a reasonable choice for the design of a privacy-preserving system. To this end, we propose PrivDA, a Privacy-preserving IoT Data Aggregation scheme based on the blockchain and homomorphic encryption technologies. In the proposed system, each data consumer can create a smart contract and publish both terms of service and requested IoT data. Thus, the smart contract puts together into one group potential data producers that can answer the consumer’s request and chooses one aggregator, the role of which is to compute the group requested result using homomorphic computations. Therefore, group-level aggregation obfuscates IoT data, which complicates sensitive information inference from a single IoT device. Finally, we deploy the proposal on a private Ethereum blockchain and give the performance evaluation.

Quantum Error Correction Code Scheme used for Homomorphic Encryption like Quantum Computation

2019 ◽  
Vol 19 (3) ◽  
pp. 61-70
Author(s):  
Il Kwon Sohn ◽  
◽  
Jonghyun Lee ◽  
Wonhyuk Lee ◽  
Woojin Seok ◽  
...  
Keyword(s):  
Error Correction ◽  
Quantum Computation ◽  
Homomorphic Encryption ◽  
Quantum Error Correction ◽  
Code Scheme ◽  
Error Correction Code ◽  
Quantum Error

Heuristic vs Metaheuristic Method: Improvement of Spoofed Fingerprint Identification in IoT Devices

2019 ◽  
Vol 12 (3) ◽  
pp. 168
Author(s):  
Guruh Fajar Shidik ◽  
Edi Jaya Kusuma ◽  
Safira Nuraisha ◽  
Pulung Nurtantio Andono
Keyword(s):  
Fingerprint Identification ◽  
Iot Devices ◽  
Method Improvement
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