Bitcoin mempool growth and trading volumes: Integrated approach based on QROF Multi-SWARA and aggregation operators

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing. 

Blockchain-based Secure Outsourcing Data Integrity Auditing for Internet of Things in Cloud-edge Environment

Computing the determinant of large matrix is a time-consuming task, which is appearing more and more widely in science and engineering problems in the era of big data. Fortunately, cloud computing can provide large storage and computation resources, and thus, act as an ideal platform to complete computation outsourced from resource-constrained devices. However, cloud computing also causes security issues. For example, the curious cloud may spy on user privacy through outsourced data. The malicious cloud violating computing scripts, as well as cloud hardware failure, will lead to incorrect results. Therefore, we propose a secure outsourcing algorithm to compute the determinant of large matrix under the malicious cloud mode in this paper. The algorithm protects the privacy of the original matrix by applying row/column permutation and other transformations to the matrix. To resist malicious cheating on the computation tasks, a new verification method is utilized in our algorithm. Unlike previous algorithms that require multiple rounds of verification, our verification requires only one round without trading off the cheating detectability, which greatly reduces the local computation burden. Both theoretical and experimental analysis demonstrate that our algorithm achieves a better efficiency on local users than previous ones on various dimensions of matrices, without sacrificing the security requirements in terms of privacy protection and cheating detectability.

Secure Outsourcing of Matrix Determinant Computation under the Malicious Cloud

Internet of Things enables devices to communicate, collect and exchange data with the network. As the number of IoT devices keeps growing, the volume of data they produce is also increasing exponentially. Given the feature of limited computing and storage resources of IoT, it is inevitable to store data in the cloud for better services. However, for users to effectively and efficiently inspect those data over the cloud is a critical and open problem. Most public integrity auditing over the cloud schemes requires the user to do a sheer amount of preprocessing work on the local devices, which is unsuitable for IoT devices. With the development of edge computing extending cloud computing, it can provide computing capability for resource-constrained devices in close geographic proximity. In this paper, we design an auditing scheme based on secure computation outsourcing assisted by edge computing, in which the data preprocessing work can be offloaded to the edge server. The experiments show that it reduces the computing load on the devices and improves the efficiency of task processing.

The efficiency of fully homomorphic encryption has always affected its practicality. With the dawn of Internet of things, the demand for computation and encryption on resource-constrained devices is increasing. Complex cryptographic computing is a major burden for those devices, while outsourcing can provide great convenience for them. In this paper, we firstly propose a generic blockchain-based framework for secure computation outsourcing and then propose an algorithm for secure outsourcing of polynomial multiplication into the blockchain. Our algorithm for polynomial multiplication can reduce the local computation cost to . Previous work based on Fast Fourier Transform can only achieve for the local cost. Finally, we integrate the two secure outsourcing schemes for polynomial multiplication and modular exponentiation into the fully homomorphic encryption using hidden ideal lattice and get an outsourcing scheme of fully homomorphic encryption. Through security analysis, our schemes achieve the goals of privacy protection against passive attackers and cheating detection against active attackers. Experiments also demonstrate our schemes are more efficient in comparisons with the corresponding nonoutsourcing schemes.

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Blockchain-Based Secure Outsourcing of Polynomial Multiplication and Its Application in Fully Homomorphic Encryption

Blockchain is a well-known distributed technology which can be regarded as a decentralized database and combines the peer-to-peer architecture, cryptography and consensus mechanism. As the first and most famous application of blockchain and cryptocurrencies, Bitcoin also suffers from different kinds of attacks. Several studies have been done to analyze and solve these attacks. In this paper, we research the Distributed Denial-of-Service (DDoS) on Bitcoin’s memory pool (Mempool). We analyze the feasibility of this kind of attack based on the current mining and relay progress of Bitcoin, and propose the possible effects caused by this attack from some novel aspects of Bitcoin, including the confirmation time, market price and legitimate user. We further present the Dynamic Fee Threshold Mechanism to counter this attack. Our method uses several characteristics to distinguish the normal transactions in Bitcoin from the malicious spam transactions. We also make an analysis and experiments on the method, to demonstrate its effectiveness and accuracy of detecting the malicious transactions.

Preventing DDoS Attacks on Bitcoin Memory Pool by the Dynamic Fee Threshold Mechanism

The efficiency of homomorphic encryption has always affected its practicality. With the dawn of internet of things, the demand for computation and encryption on lightweight devices is increasing. Complex cryptographic computing is an important burden for lightweight devices, but outsourcing provides great convenience for them. In this paper, based on blockchain, we propose a secure outsourcing scheme for Fully Homomorphic Encryption using Hidden Ideal Lattice (FHEHIL), in which the time-consuming operations (including modular exponentiation and polynomial multiplication) are outsourced. For polynomial multiplication, we propose a secure outsourcing algorithm that reduces the local computation cost to \(O\left(n\right)\). Previous work based on Fast Fourier Transform can only achieve \(O\left(nlog(n)\right)\) for the local cost. Through security analysis, our scheme achieves the goals of privacy protection against passive attackers and cheating detection against active attackers. Experiments also demonstrate our scheme is more efficient in comparison with the non-outsourcing FHEHIL.

Blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice.

Monitoring people’s trajectories can help with many data mining tasks. For example, clustering analysis of users’ trajectories can infer where people work and where they live. However, as far as users are concerned, no one wants their privacy to be compromised so that third parties can benefit from it. The commonly used method of privacy protection today is differential privacy, but differential privacy does not have significant advantages when dealing with correlated data. Pufferfish privacy can be used to address the privacy protection of correlated data for this reason. Our work aims to protect the locations that are extracted from trajectories using clustering methods. To achieve this goal, we first use the DBSCAN algorithm to cluster the trajectories of users, mark the locations where users have stayed, and preserve the chronological order. Privacy requirements for this issue are then specified in the Pufferfish framework. The data is then processed using a mechanism that implements a privacy framework. Finally, the utility is evaluated through experiments.

Mingyang Song

Papers

Blockchain-Based Secure Outsourcing of Polynomial Multiplication and Its Application in Fully Homomorphic Encryption

Preventing DDoS Attacks on Bitcoin Memory Pool by the Dynamic Fee Threshold Mechanism

Blockchain-Based Secure Outsourcing of Fully Homomorphic Encryption Using Hidden Ideal Lattice.

A Pufferfish Privacy Mechanism for the Trajectory Clustering Task