An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

Ramesh, Shruthi; Manimaran, G.

doi:10.1109/jiot.2020.2998109

Cited by 26 publications

(10 citation statements)

References 44 publications

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“…Adaptation and secure Identity-based broadcast encryption systems are, in the standard model, equipped with a certain size cipher text [4]. The secret key of the size of the public key is linear to the maximum number of recipients [5]. Also, our system is completely rigging, has a stateless receiver.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Computation Architecture for Big Data Analytics Using Encryption Algorithm

Banasode¹,

Padmannavar²

2021

INDJCSE

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Big data infrastructure needs to be structured in the most critical aspects and wisely calculate how large data applications are managed in order to achieve the most important security issues required. One of them is privacy is a related feature as users can share more and more personal data and content and public clouds on social networks through their devices and computers. The previous system have some drawbacks, it is not secure the sensitive data storage security of privacy issues in the big data. However, the existing encryption system for data cannot protect the access mode, and it can also leak sensitive information. To overcome the issues in this work proposed the method Transparent Secure Hashing Data Optimized Privacy Protection Encryption (TSHDOPPE) Algorithm for Encryption of data can prevent permission to use the unwanted users' associated data storage system of data. Less storage leakage overhead and efficiency are proposed. It includes stored in a transparent data protected for data at rest and could not leak the Data for data in Transit. Non-relational data storage and protection data storage has been a TSHDOPPE algorithm used to ensure the transaction log. In the data used for stored in cloud computing, managing user data optimizing the Improved Deep Neural Network (IDNN) is reduces difficult and reduces the cost of maintaining data. Unprotected data in Transit or at rest, or vulnerable to attacks, companies are also effective security measures that provide protected data with strong data protection between the device and the network in these conditions.

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Section: Related Workmentioning

confidence: 99%

Enhanced Computation Architecture for Big Data Analytics Using Encryption Algorithm

Banasode¹,

Padmannavar²

2021

INDJCSE

View full text Add to dashboard Cite

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“…Interleaved multiplication is used to construct this adder-based design [9], which was described before. The central processing unit employs a Montgomery MM algorithm that has a multiplier that is r bits by r bits [10]. A multiplier that is n bits by n bits is used in the construction of the processors [11].…”

Section: Introductionmentioning

confidence: 99%

SLDEB: Design of a Secure and Lightweight Dynamic Encryption Bio-Inspired Model for IoT Networks

Kataria¹,

Jethva²,

Shinde³

et al. 2023

IJSSE

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Designing encryption models for IoT deployments requires analysis of multiple network level constraints. These include, estimation of energy requirements, security strength, encryption & decryption delay, computational complexity, etc. A wide variety of models are proposed to perform these tasks, but most of them are either highly complex, or require higher energy levels for encrypting data samples. Moreover, these models are contextindependent, and cannot be used for application-specific deployments. To overcome these issues, this text proposes design of a novel secure and lightweight dynamic encryption bioinspired model for IoT networks. The proposed model initially uses an Elliptic Curve Cryptography (ECC) process for data security, and optimizes its performance via Bacterial Foraging Optimization (BFO). ECC parameters that are obtained via BFO are further fine-tuned using a Q-Learning based process, which assists in identification of context-specific parametric ranges for different network types. The combination of BFO with Q-Learning results in dynamic ECC curves, which can be used for context-specific deployments. Performance of the model was evaluated on different scaled networks, and compared with other state-of-the-art encryption models in terms of encryption delay, decryption delay, security level under different attacks, and energy consumption levels. Based on this comparison, it was observed that the proposed model showcased 8.5% lower encryption delay, 3.2% lower decryption delay, and 5.9% lower energy consumption while maintaining similar security levels. Due to these enhancements, the proposed model is useful for a wide variety of low complexity IoT deployments.

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“…Internet of ings (IoT) devices are very common in our daily life, and more and more physical entities are connected to cyberspace. A series of intelligent applications derived from IoT such as smart home, smart medical, and smart grid are profoundly changing social public services and people's daily life [1]. As a key biometric technology to authenticate personal identity, facial recognition and resolution technology can collect any object that needs monitoring and interaction through information sensors.…”

Section: Introductionmentioning

confidence: 99%

“…RS 􏼁 􏼁,(1) which binds public key and identity.Similarly, MS has a unique ID MS , x MS , y MS � g x MS modq, authorization certificate…”

mentioning

confidence: 99%

A Privacy Protection Scheme for Facial Recognition and Resolution Based on Edge Computing

Feng

Liang

et al. 2022

Security and Communication Networks

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Facial recognition and resolution technology have extensive application scenarios in the era of big data. It ensures the consistency of personal identity in physical space and cyberspace by establishing correspondence between physical objects and network entities. However, massive data brings huge processing pressure to cloud service, and there are data leakage risks about personal information. To address this problem, we propose a privacy security protection scheme for facial recognition and resolution based on edge computing. Firstly, a facial recognition and resolution framework based on edge computing is established, which improves the communication and storage efficiency through task partition and relieves the pressure of cloud computing. Then, a verifiable deletion scheme based on Hidden CP-ABE is proposed to provide fine-grained access control and ensure the safe deletion of target data in the cloud. Moreover, after applying the verifiable deletion method, the safe deletion of the target data in the cloud can be achieved. Finally, the simulation results show the effectiveness and security of the proposed scheme.

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An Efficient Framework for Privacy-Preserving Computations on Encrypted IoT Data

Cited by 26 publications

References 44 publications

Enhanced Computation Architecture for Big Data Analytics Using Encryption Algorithm

Enhanced Computation Architecture for Big Data Analytics Using Encryption Algorithm

SLDEB: Design of a Secure and Lightweight Dynamic Encryption Bio-Inspired Model for IoT Networks

A Privacy Protection Scheme for Facial Recognition and Resolution Based on Edge Computing

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