Coin mixing methods are widely used to enhance anonymity in cryptocurrency transactions by obfuscating the linkages between recipients and senders. Specifically, coin mixing methods combine several users' transactions into a CoinJoin transaction and decompose the original transactions' outputs into a set of decomposed outputs with similar amounts. However, existing methods have two shortcomings. Firstly, CoinJoin transactions lack anonymity guarantees. Secondly, the number of decomposed outputs is not minimized. To tackle these two shortcomings, we develop a platform named CMixing for mixing transactions with anonymity guarantees and minimal fees. For a CoinJoin transaction obtained by CMixing, the probability of adversaries correctly guessing the original output of a decomposed output does not exceed c , where c is a privacy requirement. Thus, the first shortcoming is solved. Additionally, CMixing uses an approximation algorithm to decompose original outputs, which approximately minimizes the number of decomposed outputs. Thus, the second shortcoming is solved. Our demonstration will showcase how users can use CMixing to make CoinJoin transactions. We will also show the fees saved and the level of anonymity achieved using our algorithm.

The payment channel network (PCN) is a promising solution to increase the throughput of blockchains. However, unidirectional transactions can deplete a user's deposits in a payment channel (PC), reducing the success ratio of transactions (SRoT). To address this depletion issue, rebalance protocols are used to shift tokens from well-deposited PCs to under-deposited PCs. To improve SRoT, it is beneficial to increase the balance of a PC with a lower balance and a higher weight (i.e., more transaction executions rely on the PC). In this paper, we define the utility of a transaction and the utility-aware rebalance (UAR) problem. The utility of a transaction is proportional to the weight of the PC and the amount of the transaction, and inversely proportional to the balance of the receiver. To maximize the effect of improving SRoT, UAR aims to find a set of transactions with maximized utilities, satisfying the budget and conservation constraints. The budget constraint limits the number of tokens shifted in a PC. The conservation constraint requires that the number of tokens each user sends equals the number of tokens received. We prove that UAR is NP-hard and cannot be approximately solved with a constant ratio. Thus, we propose two heuristic algorithms, namely Circuit Greedy and UAR_DC. Extensive experiments show that our approaches outperform the existing approach by at least 3.16 times in terms of utilities.

Recently, more and more higher education institutions have been using student feedback questionnaires (SFQ) to evaluate teaching. However, existing SFQ systems have two shortcomings. The first is that the respondent of an SFQ is not anonymous. The second is that the statistical report of SFQs can be manipulated. To tackle these two shortcomings, we develop a novel SFQ system, namely PSFQ. In PSFQ, the respondent of an SFQ is mixed with multiple users by a ring signature. PSFQ uses an advanced ring signature approach to minimize the size of a ring signature when anonymity satisfies the requirements. Thus, the first shortcoming has been overcome. Moreover, all answers are encrypted by homomorphic encryption and stored on the blockchain, enabling users to verify the correctness of the statistical reports. Our demonstration will showcase how PSFQ provides confidential SFQ responses while ensuring the correctness of statistical reports.