The proof-of-stake (PoS) protocols aim to reduce the unnecessary computing power waste seen in Bitcoin. Various practical and provably secure designs have been proposed, like Ouroboros Praos (Eurocrypt 2018) and Snow White (FC 2019). However, the essential security property of unpredictability in these protocols remains insufficiently explored. This paper delves into this property in the cryptographic setting to achieve the "best possible" unpredictability for PoS protocols.We first present an impossibility result for all PoS protocols under the single-extension design framework, where each honest player extends one chain per round. The state-of-the-art permissionless PoS protocols (e.g., Praos, Snow White, and more), are all under this single-extension framework. Our impossibility result states that, if a single-extension PoS protocol achieves the best possible unpredictability, then this protocol cannot be proven secure unless more than 73% of stake is honest.To overcome this impossibility, we introduce a new design framework called multi-extension PoS, allowing each honest player to extend multiple chains using greedy strategy in a round. This strategy allows us to construct a class of PoS protocols that achieve the best possible unpredictability. Additionally, we design a new tiebreak rule for the multi-extension protocol to choose the best chain that can be extended faster, ensuring that the adversary cannot slow-down the chain growth of honest players. It is noteworthy that these protocols can be proven secure, assuming a much smaller fraction (e.g., 57%) of stake to be honest.For a comprehensive security analysis in the cryptographic setting, we develop several new techniques. Analyzing chain growth becomes highly non-trivial as players can extend multiple chains. We introduce a new analysis framework using the Markov chain to assess the chain growth of a multi-extension protocol. To prove the common prefix property, we introduce a concept called "virtual chains" and present a reduction from the regular version of the common prefix to "common prefix w.r.t. virtual chains."

The proof-of-stake (PoS) protocols aim to reduce the unnecessary computing power waste seen in Bitcoin. Various practical and provably secure designs have been proposed, like Ouroboros Praos (Eurocrypt 2018) and Snow White (FC 2019). However, the essential security property of unpredictability in these protocols remains insufficiently explored. This paper delves into this property in the cryptographic setting to achieve the "best possible" unpredictability for PoS. We first present an impossibility result for all PoS protocols under the single-extension design framework, where each honest player extends one chain per round. The state-of-the-art permissionless PoS protocols (e.g., Praos, Snow White, and more), are all under this single-extension framework. Our impossibility result states that, if a single-extension PoS protocol achieves the best possible unpredictability, then this protocol cannot be proven secure unless more than 73% of stake is honest. To overcome this impossibility, we introduce a new design framework called multi-extension PoS, allowing each honest player to extend multiple chains using a greedy strategy in a round. This strategy allows us to construct a class of PoS protocols that achieve the best possible unpredictability. It is noteworthy that these protocols can be proven secure, assuming a much smaller fraction (e.g., 57%) of stake to be honest.

Mistrusting nodes in a blockchain can reach consensus without the need of a trusted central entity. Instead, the nodes reach consensus through exchanging information on a peer-to-peer (P2P) network, without pre-established identities. Serving as the foundation of the blockchain, the P2P network plays critical roles in all performance and security aspects of the blockchain system. While P2P networks had been previously examined for many applications domains, including the file sharing systems, there is relatively less understanding on blockchain P2P networks that differs substantially from traditional P2P systems. In this chapter, we will cover different aspects of blockchain P2P networks from topology, peer discovery, known attacks, and defenses to improvement proposals to increase the throughput and reduce the latency in blockchain. Finally, we investigate theoretical limit on the throughput of blockchain systems in which nodes have heterogeneous capacities. We provide insights and discussion on how to construct a network to achieve the maximum theoretical limit in throughput.

Quantum annealing (QA) that encodes optimization problems into Hamiltonians remains the only near-term quantum computing paradigm that provides sufficient many qubits for real-world applications. To fit larger optimization instances on existing quantum annealers, reducing Hamiltonians into smaller equivalent Hamiltonians provides a promising approach. Unfortunately, existing reduction techniques are either computationally expensive or ineffective in practice. To this end, we introduce a novel notion of non-separable group, defined as a subset of qubits in a Hamiltonian that obtains the same value in optimal solutions. We develop non-separability theory accordingly and propose FastHare, a highly efficient reduction method. FastHare, iteratively, detects and merges non-separable groups into single qubits. It does so within a provable worst-case time complexity of only O(αn 2 ), for some user-defined parameter α. Our extensive benchmarks for the feasibility of the reduction are done on both synthetic Hamiltonians and 3000+ instances from the MQLIB library. The results show FastHare outperforms the roof duality, the implemented reduction in D-Wave's library. It demonstrates a high level of effectiveness with an average of 62% qubits saving and 0.3s processing time, advocating for Hamiltonian reduction as an inexpensive necessity for QA.

Ranking nodes based on their centrality stands a fundamental, yet, challenging problem in large-scale networks. Approximate methods can quickly estimate nodes' centrality and identify the most central nodes, but the ranking for the majority of remaining nodes may be meaningless. For example, ranking for less-known websites in search queries is known to be noisy and unstable. To this end, we investigate a new node ranking problem with two important distinctions: a) ranking quality, rather than the centrality estimation quality, as the primary objective; and b) ranking only nodes of interest, e.g., websites that matched search criteria. We propose Sample space Partitioning Hypothesis Ranking, or SaPHyRa, that transforms node ranking into a hypothesis ranking in machine learning. This transformation maps nodes' centrality to the expected risks of hypotheses, opening doors for theoretical machine learning (ML) tools. The key of SaPHyRa is to partition the sample space into exact and approximate subspaces. The exact subspace contains samples related to the nodes of interest, increasing both estimation and ranking qualities. The approximate space can be efficiently sampled with ML-based techniques to provide theoretical guarantees on the estimation error. Lastly, we present SaPHyRabc, an illustration of SaPHyRa on ranking nodes' betweenness centrality (BC). By combining a novel bi-component sampling, a 2-hop sample partitioning, and improved bounds on the Vapnik-Chervonenkis dimension, SaPHyRa(bc) can effectively rank any node subset in BC. Its performance is up to 200x faster than state-of-the-art methods in approximating BC, while its rank correlation to the ground truth is improved by multifold.

Despite the great potential of Federated Learning (FL) in large-scale distributed learning, the current system is still subject to several privacy issues due to the fact that local models trained by clients are exposed to the central server. Consequently, secure aggregation protocols for FL have been developed to conceal the local models from the server. However, we show that, by manipulating the client selection process, the server can circumvent the secure aggregation to learn the local models of a victim client, indicating that secure aggregation alone is inadequate for privacy protection. To tackle this issue, we leverage blockchain technology to propose a verifiable client selection protocol. Owing to the immutability and transparency of blockchain, our proposed protocol enforces a random selection of clients, making the server unable to control the selection process at its discretion. We present security proofs showing that our protocol is secure against this attack. Additionally, we conduct several experiments on an Ethereum-like blockchain to demonstrate the feasibility and practicality of our solution.

Non-interactive zero-knowledge proof or argument (NIZK) systems are widely used in many security sensitive applications to enhance computation integrity, privacy and scalability. In such systems, a prover wants to convince one or more verifiers that the result of a public function is correctly computed without revealing the (potential) private input, such as the witness. In this work, we introduce a new notion, called scriptable SNARK, where the prover and verifier(s) can specify the function (or language instance) to be proven via a script. We formalize this notion in UC framework and provide a generic trusted hardware based solution. We then instantiate our solution in both SGX and Trustzone with Lua script engine. The system can be easily used by typical programmers without any cryptographic background. The benchmark result shows that our solution is better than all the known SNARK proof systems w.r.t. prover’s running time (1000 times faster), verifier’s running time, and the proof size. In addition, we also give a lightweight scriptable SNARK protocol for hardware with limited state, e.g., Θ ( λ ) bits. Finally, we show how the proposed scriptable SNARK can be readily deployed to solve many well-known problems in the blockchain context, e.g. verifier’s dilemma, fast joining for new players, etc.

Non-interactive zero-knowledge proof or argument (NIZK) systems are widely used in many security sensitive applications to enhance computation integrity, privacy and scalability. In such systems, a prover wants to convince one or more verifiers that the result of a public function is correctly computed without revealing the (potential) private input, such as the witness. In this work, we introduce a new notion, called succinct scriptable NIZK, where the prover and verifier(s) can specify the function (or language instance) to be proven via a script. We formalize this notion is UC framework and provide a generic trusted hardware based solution. We then instantiate our solution in both SGX and Trustzone with Lua script engine. The system can be easily used by typical programmers without any cryptographic background. The benchmark result shows that our solution is better than all the known NIZK proof systems w.r.t. prover’s running time (1000 times faster), verifier’s running time, and the proof size. Finally, we show how the proposed scriptable succinct NIZK can be readily deployed to solve many well-known problems in the blockchain context, e.g. verifier’s dilemma, fast joining for new players, etc..

Biologists seek to identify a small number of significant features that are important, non-redundant, and relevant from diverse omics data. For example, statistical methods such as LIMMA and DEseq distinguish differentially expressed genes between a case and control group from the transcript profile. Researchers also apply various column subset selection algorithms on genomics datasets for a similar purpose. Unfortunately, genes selected by such statistical or machine learning methods are often highly co-regulated, making their performance inconsistent. Here, we introduce a novel feature selection algorithm that selects highly disease-related and non-redundant features from a diverse set of omics datasets. We successfully applied this algorithm to three different biological problems: (a) disease-to-normal sample classification; (b) multiclass classification of different disease samples; and (c) disease subtypes detection. Considering the classification of ROC-AUC, false-positive, and false-negative rates, our algorithm outperformed other gene selection and differential expression (DE) methods for all six types of cancer datasets from TCGA considered here for binary and multiclass classification problems. Moreover, genes picked by our algorithm improved the disease subtyping accuracy for four different cancer types over state-of-the-art methods. Hence, we posit that our proposed feature reduction method can support the community to solve various problems, including the selection of disease-specific biomarkers, precision medicine design, and disease sub-type detection.

To eliminate the unnecessary waste of energy and computing power in Bitcoin, in this paper, we develop a novel proof-of-stake consensus in the permissionless se‹ing. Among other features, our design achieves the “best possible” unpredictability for permissionless proof-of-stake protocols. As shown by Brown-Cohen et al (EC 2019), unpredictability property is critical for proof-of-stake consensus in the rational se‹ing; the ƒip side of unpredictability property, i.e., predictability can be abused by the a‹ackers for launching strengthened version of multiple a‹acks such as sel€sh-mining and bribing, against proof-of-stake systems. We are inspired by Bitcoin’s “block-by-block” design, and we show that a direct and natural mimic of Bitcoin’s design via proof-of-stake is secure if the majority 73% of stake is honest. Our result relies on an interesting upper bound of extending proof-of-stake blockchain we establish: players (who may extend all chains) can generate blockchain at most 2.72× faster than playing the basic strategy of extending the longest chain. We introduce a novel strategy called “D-distance-greedy” strategy, which enables us to construct a class of secure proof-of-stake blockchain protocols, against an arbitrary adversary, even assuming much smaller (than 73% of) stake is honest. To enable a thorough security analysis in the cryptographic se‹ing, we develop several new techniques: for example, to show the chain growth property, we represent the chain extension process via a Markov chain, and then develop a random walk on the Markov chain; to prove the common pre€x property, we introduce a new concept called “virtual chains”, and then present a reduction from the regular version of common pre€x to “common pre€x w.r.t. virtual chains”. Finally, we note that, ours is the €rst “block-by-block” style of proof-of-stake in the permissionless se‹ing, naturally mimicking Bitcoin’s design; it turns out that this feature, again allows us to achieve the “best possible” unpredictability property. Other existing provably secure permissionless proof-of-stake solutions are all in an “epoch-by-epoch” style, and thus cannot achieve the best possible unpredictability. ∗‘is is a replacement of earlier versions of the work [30, 31, 29]. Several technical issues in previous versions have been addressed; please see Section 6.3 for details. †Shanghai Jiaotong University, Email: fanlei@sjtu.edu.cn ‡University of Maryland, Email: jkatz@cs.umd.edu §Virginia Commonwealth University, Email: thaipd@vcu.edu Virginia Commonwealth University, Email: hszhou@vcu.edu

Bitcoin-like blockchains use a proof-of-work (PoW) mechanism, where security holds if the majority of the computing power is under the control of honest players. However, this assumption has been seriously challenged recently, and Bitcoin-like systems fail if this assumption is violated. In this work we propose a novel 2-hop blockchain protocol that combines PoW and proof-of-stake (PoS) mechanisms. Our analysis shows that the protocol is secure as long as the honest players control a majority of the collective resources (which consist of both computing power and stake). In particular, even if the adversary controls more than 50% of the computing power, security still holds if the honest parties hold sufficiently high stake in the system. As an added contribution, our protocol also remains secure against adaptive adversaries.

Despite many scaling proposals for Bitcoin protocols, existing permissionless approaches, including BitcoinCompact, Bitcoin-NG, and Conflux, achieve very low efficiency in terms of networking. In our large-scale peer-to-peer blockchain simulation, no existing permissionless protocols can achieve more than 4% bandwidth utilization, the fraction of bandwidth used for transmitting confirmed transactions. We propose BackPackers, a cross-layer paradigm that optimize concurrently both consensus (layer 1) and network communication (layer 0) protocols. BackPackers introduces a new node role, called packers, who form a secure and decentralized network backbone. Without any trust assumption, independent packers work together to effectively distribute transactions to all miners, eliminating a major network bottleneck in broadcasting transactions. In exchange for their networking service, each packer receives a portion of transaction fees that it distributes. Through theoretical analysis, we show rigorous proofs for security properties, namely, consistency and liveness. Most importantly, we prove that BackPackers achieves (1− )-optimality in throughput, with respect to the network limit, and O(1)-optimality in block propagation time, even when the network is heterogeneous. Through experimental studies, we show that BackPackers can achieve up to 80% bandwidth utilization, achieving 8,000+ tps and 1.2s block propagation time for 1,000 nodes with 20Mbps bandwidth. Under a same networking condition, BackPackers achieves an order of magnitude higher throughput comparing to the state-of-the-arts permissionless blockchains.

Given a weighted hypergraph ℋ(V, ℰ⊆ 2^V, w), the approximate k-cover problem seeks for a size-k subset of V that has the maximum weighted coverage by sampling only a few hyperedges in ℰ. The problem has emerged from several network analysis applications including viral marketing, centrality maximization, and landmark selection. Despite many efforts, even the best approaches require O(k n log n) space complexities, thus, cannot scale to, nowadays, humongous networks without sacrificing formal guarantees. In this paper, we propose BCA, a family of algorithms for approximate k-cover that can find (1-1/e -ϵ)-approximation solutions within an O(ϵ^-2n log n) space. That is a factor k reduction on space comparing to the state-of-the-art approaches with the same guarantee. We further make BCA more efficient and robust on real-world instances by introducing a novel adaptive sampling scheme, termed DTA.

Proteins do not independently work, they interact with each other. Their interactions are modelled by protein-protein interaction (PPI) networks. PPI networks alignment problem has great significance in the biomedical field and has attracted the interest of many researchers. This article introduces a new algorithm called ACOGNA2 to global aligning the PPI networks. Experiments demonstrated that the proposed algorithm outperform the state-of-the-art algorithms.

Quantifying users’ influence in social networks is an important topic with many applications including viral marketing, political studies, and fake news propagation. For the last few years, the novel sketching technique, termed RIS, by Borgs. et al. has inspired a series of scalable approaches for influence analysis. In this paper, we propose a new sketching technique, termed hop-based influence sketches or HIS that provides a more compact yet more accurate estimation. Unlike ad hoc heuristics, HIS-based approaches can solve influence estimation and influence maximization tasks with rigorous statistical guarantees on errors. Finally, we provide initial experiments on real-world networks to demonstrate the efficiency of our new sketch comparing to the existing influence sketches.

Reconstruction of founder (ancestor) genes for a given population is an important problem in evolutionary biology. It involves finding a set of genes that can combine together to form genes of all individuals in that population. Such reconstruction can be modeled as a combinatorial optimization problem, in which we have to find a set of founder (gene) sequences so that the individuals in a given population can be generated by the smallest number of recombination on these founder sequences. In this paper we propose a novel ant colony optimization algorithm (ACO) based method, equipped with some important improvements, for the founder gene sequence reconstruction problem. The proposed method yields excellent performance when validating on 108 test sets from three benchmark datasets. Comparing with the best by far method for founder sequence reconstruction, our proposed method performs better in 45 test sets, equally well in 44 and worse only in 19 sets. These experimental results demonstrate the efficacy and perspective of our proposed method.

Protein structure prediction is considered as one of the most long-standing and challenging problem in bioinformatics. In this paper, we present an efficient ant colony optimization algorithm to predict the protein structure on three-dimensional face-centered cubic lattice coordinates, using the hydrophobic-polar model and the Miyazawa-Jernigan model to calculate the free energy. The reinforcement learning information is expressed in the k-order Markov model, and the heuristic information is determined based on the increase of the total energy. On a set of benchmark proteins, the results show a remarkable efficiency of our algorithm in comparison with several state-of-the-art algorithms.

Cryptocurrencies have recently gained huge popularity. It is desirable to come up with effective approaches to constructing better blockchain protocols. In this paper, inspired by the 2-hop design by Duong et al (ePrint 2016/716), we put forth a generic paradigm for blockchain design, called n-hop blockchain. It includes one main chain, which is supported by (n - 1) supporting chains; hence, the main chain can achieve better security performance. In our paradigm, we show that our n-hop design can be easily extended to (n + 1)-hop design. To demonstrate the power of our paradigm, we showcase two instantiations: 2-hop blockchain variant, a combination of proof-of-stake and proof-of-work, and 3-hop blockchain variant, which is extended from 2-hop blockchain variant by adding Byzantine fault tolerance blockchain in 3rd hop.