Blockchain Privacy and Scalability in a Decentralized Validated Energy Trading Context with Hyperledger Fabric.

Lower renewable energy generator prices are leading people to install solar panels to reduce their electricity bills or, in some cases, even sell the surplus generated energy to the grid and earn credits from the grid operator. Generally, they are limited to trading the energy they generate with the grid company, which has a dominant role in price determination. Decentralized energy markets might increase both market competitiveness and incentive to further people's adoption of renewable energy, reducing security vulnerabilities and improving resiliency. Blockchain is a widely studied technology to provide decentralization for energy markets in this context. Scalability, privacy, market design, and user security are some of the open research topics. This work analyzes the literature related to blockchain and energy markets, proposes a model, implements it, performs experiments, and analyzes network scalability and data generation. The model, implemented with Hyperledger Fabric, enables validated clean energy trading with anonymized buyers to prevent consumption pattern exposure. The maximum transaction throughput was achieved with 5000 sensors, 5000 buyers, and 5000 sellers. The data generation rate by network and the baseline deployment costs were also analyzed to judge the network viability. Furthermore, this work provides empirical results on a topic that the literature lacks.

Steganographic Analysis of Blockchains.

Steganography is one of the ways to hide data between parties. Its use can be worrisome, e.g., to hide illegal communications. Researchers found that public blockchains can be an attractive place to hide communications; however, there is not much evidence of actual use in blockchains. Besides, previous work showed a lack of steganalysis methods for blockchains. In this context, we present a steganalysis approach for blockchains, evaluating it in Bitcoin and Ethereum, both popular cryptocurrencies. The main objective is to answer if one can find steganography in real case scenarios, focusing on LSB of addresses and nonces. Our sequential analysis included 253 GiB and 107 GiB of bitcoin and ethereum, respectively. We also analyzed up to 98 million bitcoin clusters. We found that bitcoin clusters could carry up to 360 KiB of hidden data if used for such a purpose. We have not found any concrete evidence of hidden data in the blockchains. The sequential analysis may not capture the perspective of the users of the blockchain network. In this case, we recommend clustering analysis, but it depends on the clustering method's accuracy. Steganalysis is an essential aspect of blockchain security.