176 research outputs found
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
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