A game theoretic analysis on block withholding attacks using the zero-determinant strategy

In Bitcoin's incentive system that supports open mining pools, block withholding attacks incur huge security threats. In this paper, we investigate the mutual attacks among pools as this determines the macroscopic utility of the whole distributed system. Existing studies on pools' interactive attacks usually employ the conventional game theory, where the strategies of the players are considered pure and equal, neglecting the existence of powerful strategies and the corresponding favorable game results. In this study, we take advantage of the Zero-Determinant (ZD) strategy to analyze the block withholding attack between any two pools, where the ZD adopter has the unilateral control on the expected payoffs of its opponent and itself. In this case, we are faced with the following questions: who can adopt the ZD strategy? individually or simultaneously? what can the ZD player achieve? In order to answer these questions, we derive the conditions under which two pools can individually or simultaneously employ the ZD strategy and demonstrate the effectiveness. To the best of our knowledge, we are the first to use the ZD strategy to analyze the block withholding attack among pools

Prediction of complex super-secondary structure βαβ motifs based on combined features

AbstractPrediction of a complex super-secondary structure is a key step in the study of tertiary structures of proteins. The strand-loop-helix-loop-strand (βαβ) motif is an important complex super-secondary structure in proteins. Many functional sites and active sites often occur in polypeptides of βαβ motifs. Therefore, the accurate prediction of βαβ motifs is very important to recognizing protein tertiary structure and the study of protein function. In this study, the βαβ motif dataset was first constructed using the DSSP package. A statistical analysis was then performed on βαβ motifs and non-βαβ motifs. The target motif was selected, and the length of the loop-α-loop varies from 10 to 26 amino acids. The ideal fixed-length pattern comprised 32 amino acids. A Support Vector Machine algorithm was developed for predicting βαβ motifs by using the sequence information, the predicted structure and function information to express the sequence feature. The overall predictive accuracy of 5-fold cross-validation and independent test was 81.7% and 76.7%, respectively. The Matthew’s correlation coefficient of the 5-fold cross-validation and independent test are 0.63 and 0.53, respectively. Results demonstrate that the proposed method is an effective approach for predicting βαβ motifs and can be used for structure and function studies of proteins

Proof of User Similarity: the Spatial Measurer of Blockchain

Although proof of work (PoW) consensus dominates the current blockchain-based systems mostly, it has always been criticized for the uneconomic brute-force calculation. As alternatives, energy-conservation and energy-recycling mechanisms heaved in sight. In this paper, we propose proof of user similarity (PoUS), a distinct energy-recycling consensus mechanism, harnessing the valuable computing power to calculate the similarities of users, and enact the calculation results into the packing rule. However, the expensive calculation required in PoUS challenges miners in participating, and may induce plagiarism and lying risks. To resolve these issues, PoUS embraces the best-effort schema by allowing miners to compute partially. Besides, a voting mechanism based on the two-parties computation and Bayesian truth serum is proposed to guarantee privacy-preserved voting and truthful reports. Noticeably, PoUS distinguishes itself in recycling the computing power back to blockchain since it turns the resource wastage to facilitate refined cohort analysis of users, serving as the spatial measurer and enabling a searchable blockchain. We build a prototype of PoUS and compare its performance with PoW. The results show that PoUS outperforms PoW in achieving an average TPS improvement of 24.01% and an average confirmation latency reduction of 43.64%. Besides, PoUS functions well in mirroring the spatial information of users, with negligible computation time and communication cost.Comment: 12 pages,10 figure

Privacy-aware Data Trading

The growing threat of personal data breach in data trading pinpoints an urgent need to develop countermeasures for preserving individual privacy. The state-of-the-art work either endows the data collector with the responsibility of data privacy or reports only a privacy-preserving version of the data. The basic assumption of the former approach that the data collector is trustworthy does not always hold true in reality, whereas the latter approach reduces the value of data. In this paper, we investigate the privacy leakage issue from the root source. Specifically, we take a fresh look to reverse the inferior position of the data provider by making her dominate the game with the collector to solve the dilemma in data trading. To that aim, we propose the noisy-sequentially zero-determinant (NSZD) strategies by tailoring the classical zero-determinant strategies, originally designed for the simultaneous-move game, to adapt to the noisy sequential game. NSZD strategies can empower the data provider to unilaterally set the expected payoff of the data collector or enforce a positive relationship between her and the data collector's expected payoffs. Both strategies can stimulate a rational data collector to behave honestly, boosting a healthy data trading market. Numerical simulations are used to examine the impacts of key parameters and the feasible region where the data provider can be an NSZD player. Finally, we prove that the data collector cannot employ NSZD to further dominate the data market for deteriorating privacy leakage.Comment: 10 pages, 11 figure

Federated Learning Hyper-Parameter Tuning from a System Perspective

Federated learning (FL) is a distributed model training paradigm that preserves clients' data privacy. It has gained tremendous attention from both academia and industry. FL hyper-parameters (e.g., the number of selected clients and the number of training passes) significantly affect the training overhead in terms of computation time, transmission time, computation load, and transmission load. However, the current practice of manually selecting FL hyper-parameters imposes a heavy burden on FL practitioners because applications have different training preferences. In this paper, we propose FedTune, an automatic FL hyper-parameter tuning algorithm tailored to applications' diverse system requirements in FL training. FedTune iteratively adjusts FL hyper-parameters during FL training and can be easily integrated into existing FL systems. Through extensive evaluations of FedTune for diverse applications and FL aggregation algorithms, we show that FedTune is lightweight and effective, achieving 8.48%-26.75% system overhead reduction compared to using fixed FL hyper-parameters. This paper assists FL practitioners in designing high-performance FL training solutions. The source code of FedTune is available at https://github.com/DataSysTech/FedTune.Comment: arXiv admin note: substantial text overlap with arXiv:2110.0306