Researches on Wideband Sensing and Security of Blockchains

Abstract

In this dissertation, we discuss two research fields. One is wideband signal sensing via sub-Nyquist sampling of ultra-wideband multiband signals, and the other is the security analysis of blockchains via the profitability analysis of double-spending attacks. In each of the two fields, we provide new results by the virtue of approaching research problems in novel perspectives. In the field of sub-Nyquist sampling of ultra-wideband multiband signals, we propose a novel idea, intentional aliasing method to improve the sampling performance of a sub-Nyquist sampling system, called modulated wideband converter (MWC). MWCs have been designed to exploit a set of fast alternating pseudo random (PR) signals. Through parallel analog channels, an MWC compresses a multiband spectrum by mixing it with PR signals in the time domain, and acquires its sub-Nyquist samples. Previously, the ratio of compression was fully dependent on the specifications of PR signals. That is, to further reduce the sampling rate without information loss, faster and longer-period PR signals were needed. The implementation of such PR signal generators however results in high power consumption and large fabrication area. With practical PR signals with low complexity, the proposed intentional aliasing method is adopted to improve the ratio of compression, which results in aliased modulated wideband converter (AMWC). AMWC can further reduce the sampling rate of MWC with fixed PR signals. The main idea is to induce intentional signal aliasing at the analog-to-digital converter (ADC). In addition to the first spectral compression by the signal mixer, the intentional aliasing compresses the mixed spectrum once again. We demonstrate that AMWC reduces the number of analog channels and the rate of ADC for lossless sub-Nyquist sampling without needing to upgrade the speed or the period of PR signals. Conversely, for a given fixed number of analog channels and sampling rate, AMWC significantly improves the performance of signal reconstruction. In the field of profitability analysis of double-spending attacks on blockchains, we provide new mathematical tools for a precise profitability analysis, which enables us to propose an algorithm for optimization of user parameters utilized to prevent double-spending (DS) attacks. It was well understood that a successful DS attack is established when the proportion of computing power an attacker possesses is higher than that of the honest network. What is not yet well understood is how threatening a DS attack with less than 50% computing power used can be. Namely, DS attacks at any proportion can be a threat as long as the chance to make a good profit exists. Profit is obtained when the revenue from making a successful DS attack is greater than the cost of carrying out one. We have developed a novel probability theory for calculating a finite time attack probability. This can be used to size up attack resources needed to obtain the profit. The results enable us to derive a sufficient and necessary condition on the value of a transaction targeted by a DS attack. Our result is quite surprising: we theoretically show how a DS attack at any proportion of computing power can be made profitable. Given one’s transaction value, the results can also be used to assess the risk of a DS attack. An example of profitable DS attack against BitcoinCash is provided. The results in the two fields can be ingegrated and utilized in a field of the Internet of things (IoT). To deal with huge amounts of data, IoT applications need energy-efficient sensors and secure data management system. The intentional aliasing method contributes to improve the efficiency of sensors, and the profitability analysis of double-spending attacks contributes to improve the security of data management by blockchains.