Vertical Federated Learning (VFL) enables multiple clients to collaboratively train a global model over vertically partitioned data without leaking private local information. Tree-based models, like XGBoost and LightGBM, have been widely used in VFL to enhance the interpretation and efficiency of training. However, there is a fundamental lack of research on how to conduct VFL securely over distributed labels. This work is the first to fill this gap by designing a novel protocol, called FEVERLESS, based on XGBoost. FEVERLESS leverages secure aggregation via information masking technique and global differential privacy provided by a fairly and randomly selected noise leader to prevent private information from being leaked in the training process. Furthermore, it provides label and data privacy against honest-but-curious adversaries even in the case of collusion of$n - 2$out of n clients. We present a comprehensive security and efficiency analysis for our design, and the empirical results from our experiments demonstrate that FEVERLESS is fast and secure. In particular, it outperforms the solution based on additive homomorphic encryption in runtime cost and provides better accuracy than the local differential privacy approach.

In this work, we study the blockchain leader election problem. The purpose of such protocols is to elect a leader who decides on the next block to be appended to the blockchain, for each block proposal round. Solutions to this problem are vital for the security of blockchain systems. We introduce an efficient blockchain leader election method with security based solely on standard assumptions for cryptographic hash functions (rather than public-key cryptographic assumptions) and that does not involve a racing condition as in Proof-of-Work based approaches. Thanks to the former feature, our solution provides the highest confidence in security, even in the post-quantum era. A particularly scalable application of our solution is in the Proof-of-Stake setting, and we investigate our solution in the Algorand blockchain system. We believe our leader election approach can be easily adapted to a range of other blockchain settings.

Graph Neural Networks (GNNs) have achieved promising performance in various real-world applications. Building a powerful GNN model is not a trivial task, as it requires a large amount of training data, powerful computing resources, and human expertise. Moreover, with the development of adversarial attacks, e.g., model stealing attacks, GNNs raise challenges to model authentication. To avoid copyright infringement on GNNs, verifying the ownership of the GNN models is necessary.This paper presents a watermarking framework for GNNs for both graph and node classification tasks. We 1) design two strategies to generate watermarked data for the graph classification task and one for the node classification task, 2) embed the watermark into the host model through training to obtain the watermarked GNN model, and 3) verify the ownership of the suspicious model in a black-box setting. The experiments show that our framework can verify the ownership of GNN models with a very high probability (up to 99%) for both tasks. We also explore our watermarking mechanism against an adaptive attacker with access to partial knowledge of the watermarked data. Finally, we experimentally show that our watermarking approach is robust against a state-of-the-art model extraction technique and four state-of-the-art defenses against backdoor attacks.

Payment Channel Hub (PCH) is a promising solution to the scalability issue of first-generation blockchains or cryptocurrencies such as Bitcoin. It supports off-chain payments between a sender and a receiver through an intermediary (called the tumbler). Relationship anonymity and value privacy are desirable features of privacy-preserving PCHs, which prevent the tumbler from identifying the sender and receiver pairs as well as the payment amounts. To our knowledge, all existing Bitcoin-compatible PCH constructions that guarantee relationship anonymity allow only a (predefined) fixed payment amount. Thus, to achieve payments with different amounts, they would require either multiple PCH systems or running one PCH system multiple times. Neither of these solutions would be deemed practical.In this paper, we propose the first Bitcoin-compatible PCH that achieves relationship anonymity and supports variable amounts for payment. To achieve this, we have several layers of technical constructions, each of which could be of independent interest to the community. First, we propose BlindChannel, a novel bi-directional payment channel protocol for privacy-preserving payments, where one of the channel parties is unable to see the channel balances. Then, we further propose BlindHub, a three-party (sender, tumbler, receiver) protocol for private conditional payments, where the tumbler pays to the receiver only if the sender pays to the tumbler. The appealing additional feature of BlindHub is that the tumbler cannot link the sender and the receiver while supporting a variable payment amount. To construct BlindHub, we also introduce two new cryptographic primitives as building blocks, namely Blind Adaptor Signature (BAS), and Flexible Blind Conditional Signature (FBCS). BAS is an adaptor signature protocol built on top of a blind signature scheme. FBCS is a new cryptographic notion enabling us to provide an atomic and privacy-preserving PCH. Lastly, we instantiate both BlindChannel and BlindHub protocols and present implementation results to show their practicality.

Payment channel networks (PCNs) enhance the scalability of block-chains by allowing parties to conduct transactions off-chain, i.e, without broadcasting every transaction to all blockchain participants. To conduct transactions, a sender and a receiver can either establish a direct payment channel with a funding blockchain transaction or leverage existing channels in a multi-hop payment. The security of PCNs usually relies on the synchrony of the underlying blockchain, i.e., evidence of misbehavior needs to be published on the blockchain within a time limit. Alternative payment channel proposals that do not require blockchain synchrony rely on quorum certificates and use a committee to register the transactions of a channel. However, these proposals do not support multi-hop payments, a limitation we aim to overcome.

Neural networks provide state-of-the-art results in many domains. Yet, they often require high energy and time-consuming training processes. Therefore, the research community is exploring alternative, energy-efficient approaches likespiking neural networks (SNNs). SNNs mimic brain neurons by encoding data into sparse spikes, resulting in energy-efficient computing. To exploit the properties of the SNNs, they can be trained with neuromorphic datasets that capture the differences in motion. SNNs, just like any neural network model, can be susceptible to security threats that make the model perform anomalously. One of the most crucial threats is the backdoor attacks that modify the training set to inject a trigger in some samples. After training, the neural network will perform correctly on the main task. However, under the presence of the trigger (backdoor) on an input sample, the attacker can control its behavior. The existing works on backdoor attacks consider standard datasets and not neuromorphic ones. In this paper, to the best of our knowledge, we present the first backdoor attacks on neuromorphic datasets. Due to the structure of neuromorphic datasets, we utilize two different triggers, i.e., static andmoving triggers. We then evaluate the performance of our backdoor using spiking neural networks, achieving top accuracy on both main and backdoor tasks, up to 99%.

Current permissionless cryptocurrencies such as Bitcoin suffer from a limited transaction rate and slow confirmation time, which hinders further adoption. Payment channels are one of the most promising solutions to address these problems, as they allow the parties of the channel to perform arbitrarily many payments in a peer-to-peer fashion while uploading only two transactions on the blockchain. This concept has been generalized into payment channel networks where a path of payment channels is used to settle the payment between two users that might not share a direct channel between them. However, this approach requires the active involvement of each user in the path, making the system less reliable (they might be offline), more expensive (they charge fees per payment), and slower (intermediaries need to be actively involved in the payment). To mitigate this issue, recent work has introduced the concept of virtual channels (IEEE S&P’19), which involve intermediaries only in the initial creation of a bridge between payer and payee, who can later on independently perform arbitrarily many off-chain transactions. Unfortunately, existing constructions are only available for Ethereum, as they rely on its account model and Turing-complete scripting language. The realization of virtual channels in other blockchain technologies with limited scripting capabilities, like Bitcoin, was so far considered an open challenge.In this work, we present the first virtual channel protocols that are built on the UTXO-model and require a scripting language supporting only a digital signature scheme and a timelock functionality, being thus backward compatible with virtually every cryptocurrency, including Bitcoin. We formalize the security properties of virtual channels as an ideal functionality in the Universal Composability framework and prove that our protocol constitutes a secure realization thereof. We have prototyped and evaluated our protocol on the Bitcoin blockchain, demonstrating its efficiency: for n sequential payments, they require an off-chain exchange of 9+2n transactions or a total of 3524+695n bytes, with no on-chain footprint in the optimistic case. This is a substantial improvement compared to routing payments in a payment channel network, which requires 8n transactions with a total of 3026n bytes to be exchanged.

Abstract For commitments on secrets, statistical hiding is a must when we are dealing with a long‐term secret or when the secret domain is small enough for a brute‐force attack by a powerful adversary. Unfortunately, all the Chinese Remainder Theorem‐based verifiable secret sharing schemes in the literature are either insecure or suffer from the vulnerability of computationally hiding commitments. To the best of our knowledge, there exist five such studies where two of them were already proven to be insecure. In this work, we first show that two of the remaining schemes are also insecure, that is, the schemes reveal information on the secret even when the adversary is passive. In addition, the remaining one is only secure against a computationally bounded adversary which can be a problem for secret sharing schemes requiring long‐term secret obscurity or using small secret domain. We propose a modification for the latter scheme and prove that the modified scheme is a secure verifiable secret sharing scheme against an unbounded adversary. Lastly, as an application, we show how to use the new scheme for joint random secret sharing and analyze the practicality and efficiency of the proposed schemes. Copyright © 2016 John Wiley & Sons, Ltd.