scholarly journals Hy-Bridge: A Hybrid Blockchain for Privacy-Preserving and Trustful Energy Transactions in Internet-of-Things Platforms

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 928 ◽  
Author(s):  
Mahdi Daghmehchi Firoozjaei ◽  
Ali Ghorbani ◽  
Hyoungshick Kim ◽  
JaeSeung Song
Keyword(s):  
Service Management ◽  
Energy Use ◽  
Information Leakage ◽  
Security And Privacy ◽  
User Privacy ◽  
Case Scenario ◽  
Iot Platform ◽  
Financial Transactions ◽  
Privacy Issues ◽  
Local Block

In the current centralized IoT ecosystems, all financial transactions are routed through IoT platform providers. The security and privacy issues are inevitable with an untrusted or compromised IoT platform provider. To address these issues, we propose Hy-Bridge, a hybrid blockchain-based billing and charging framework. In Hy-Bridge, the IoT platform provider plays no proxy role, and IoT users can securely and efficiently share a credit with other users. The trustful end-to-end functionality of blockchain helps us to provide accountability and reliability features in IoT transactions. Furthermore, with the blockchain-distributed consensus, we provide a credit-sharing feature for IoT users in the energy and utility market. To provide this feature, we introduce a local block framework for service management in the credit-sharing group. To preserve the IoT users’ privacy and avoid any information leakage to the main blockchain, an interconnection position, called bridge, is introduced to isolate IoT users’ peer-to-peer transactions and link the main blockchain to its subnetwork blockchain(s) in a hybrid model. To this end, a k-anonymity protection is performed on the bridge. To evaluate the performance of the introduced hybrid blockchain-based billing and charging, we simulated the energy use case scenario using Hy-Bridge. Our simulation results show that Hy-Bridge could protect user privacy with an acceptable level of information loss and CPU and memory usage.

scholarly journals BAT—Block Analytics Tool Integrated with Blockchain Based IoT Platform

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1525
Author(s):  
Chathurangi Edussuriya ◽  
Kasun Vithanage ◽  
Namila Bandara ◽  
Janaka Alawatugoda ◽  
Manjula Sandirigama ◽  
...  
Keyword(s):  
Security And Privacy ◽  
Sensitive Information ◽  
Pharmaceutical Drugs ◽  
Case Scenario ◽  
Real World Data ◽  
Data Set ◽  
Pharmaceutical Supply Chain ◽  
Iot Platform ◽  
Novel Approach ◽  
Iot Devices

The Internet of Things (IoT) is the novel paradigm of connectivity and the driving force behind state-of-the-art applications and services. However, the exponential growth of the number of IoT devices and services, their distributed nature, and scarcity of resources has increased the number of security and privacy concerns ranging from the risks of unauthorized data alterations to the potential discrimination enabled by data analytics over sensitive information. Thus, a blockchain based IoT-platform is introduced to address these issues. Built upon the tamper-proof architecture, the proposed access management mechanisms ensure the authenticity and integrity of data. Moreover, a novel approach called Block Analytics Tool (BAT), integrated with the platform is proposed to analyze and make predictions on data stored on the blockchain. BAT enables the data-analysis applications to be developed using the data stored in the platform in an optimized manner acting as an interface to off-chain processing. A pharmaceutical supply chain is used as the use case scenario to show the functionality of the proposed platform. Furthermore, a model to forecast the demand of the pharmaceutical drugs is investigated using a real-world data set to demonstrate the functionality of BAT. Finally, the performance of BAT integrated with the platform is evaluated.

scholarly journals Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

2020 ◽  
Vol 2020 (2) ◽  
pp. 5-23
Author(s):  
Sergiu Carpov ◽  
Caroline Fontaine ◽  
Damien Ligier ◽  
Renaud Sirdey
Keyword(s):  
Data Privacy ◽  
Information Leakage ◽  
Use Cases ◽  
Security And Privacy ◽  
Functional Encryption ◽  
Cryptographic Primitives ◽  
Encrypted Data ◽  
Current State ◽  
Encryption Schemes ◽  
Privacy Issues

AbstractClassification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption. These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear.In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.

scholarly journals Blockchain-enable Contact Tracing for Preserving User Privacy During COVID-19 Outbreak

2020 ◽  
Author(s):  
Md. Murshedul Arifeen ◽  
Abdullah Al Mamun ◽  
M Shamim Kaiser ◽  
Mufti Mahmud
Keyword(s):  
Security And Privacy ◽  
Contact Tracing ◽  
User Privacy ◽  
Health Organizations ◽  
Privacy Concerns ◽  
Essential Tool ◽  
Privacy Measures ◽  
Indispensable Tool ◽  
Novel Coronavirus ◽  
Privacy Issues

Contact tracing has become an indispensable tool of various extensive measures to control the spread of COVID-19 pandemic due to novel coronavirus. This essential tool helps to identify, isolate and quarantine the contacted persons of a COVID-19 patient. However, the existing contact tracing applications developed by various countries, health organizations to trace down the contacts after identifying a COVID-19 patient suffers from several security and privacy concerns. In this work, we have identified those security and privacy issues of several leading contact tracing applications and proposed a blockchain-based framework to overcome the major security and privacy challenges imposed by the applications. We have discussed the security and privacy measures that are achieved by the proposed framework to show the effectiveness against the security and privacy issues raised by the existing mobile contact tracing applications.

scholarly journals Efficient and Privacy-Aware Power Injection over AMI and Smart Grid Slice in Future 5G Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yinghui Zhang ◽  
Jiangfan Zhao ◽  
Dong Zheng
Keyword(s):  
Smart Grid ◽  
Security And Privacy ◽  
5G Networks ◽  
User Privacy ◽  
Storage Unit ◽  
Utility Company ◽  
Power Injection ◽  
Aggregation Technique ◽  
Advanced Metering ◽  
Privacy Issues

Smart grid is critical to the success of next generation of power grid, which is expected to be characterized by efficiency, cleanliness, security, and privacy. In this paper, aiming to tackle the security and privacy issues of power injection, we propose an efficient and privacy-aware power injection (EPPI) scheme suitable for advanced metering infrastructure and 5G smart grid network slice. In EPPI, each power storage unit first blinds its power injection bid and then gives the blinded bid together with a signature to the local gateway. The gateway removes a partial blind factor from each blinded bid and then sends to the utility company aggregated bid and signature by using a novel aggregation technique called hash-then-addition. The utility company can get the total amount of collected power at each time slot by removing a blind factor from the aggregated bid. Throughout the EPPI system, both the gateway and the utility company cannot know individual bids and hence user privacy is preserved. In particular, EPPI allows the utility company to check the integrity and authenticity of the collected data. Finally, extensive evaluations indicate that EPPI is secure and privacy-aware and it is efficient in terms of computation and communication cost.

Enhancing Privacy in PUF-Cash through Multiple Trusted Third Parties and Reinforcement Learning

2022 ◽  
Vol 18 (1) ◽  
pp. 1-26
Author(s):  
Georgios Fragkos ◽  
Cyrus Minwalla ◽  
Eirini Eleni Tsiropoulou ◽  
Jim Plusquellic
Keyword(s):  
User Anonymity ◽  
Third Party ◽  
Security And Privacy ◽  
Machine Learning Techniques ◽  
User Privacy ◽  
Trusted Third Party ◽  
Physical Unclonable Functions ◽  
Learning Techniques ◽  
Direct Contrast ◽  
Debit Cards

Electronic cash ( e-Cash ) is a digital alternative to physical currency such as coins and bank notes. Suitably constructed, e-Cash has the ability to offer an anonymous offline experience much akin to cash, and in direct contrast to traditional forms of payment such as credit and debit cards. Implementing security and privacy within e-Cash, i.e., preserving user anonymity while preventing counterfeiting, fraud, and double spending, is a non-trivial challenge. In this article, we propose major improvements to an e-Cash protocol, termed PUF-Cash, based on physical unclonable functions ( PUFs ). PUF-Cash was created as an offline-first, secure e-Cash scheme that preserved user anonymity in payments. In addition, PUF-Cash supports remote payments; an improvement over traditional currency. In this work, a novel multi-trusted-third-party exchange scheme is introduced, which is responsible for “blinding” Alice’s e-Cash tokens; a feature at the heart of preserving her anonymity. The exchange operations are governed by machine learning techniques which are uniquely applied to optimize user privacy, while remaining resistant to identity-revealing attacks by adversaries and trusted authorities. Federation of the single trusted third party into multiple entities distributes the workload, thereby improving performance and resiliency within the e-Cash system architecture. Experimental results indicate that improvements to PUF-Cash enhance user privacy and scalability.

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