The Bitcoin blockchain enables users to conduct transactions securely, but its performance is restricted by the need for global consensus. Payment channels, as a promising solution to this issue, overcome this limitation through off-chain transactions. Instead of conducting each transaction on-chain, they only settle the final payment balances with the underlying blockchain. However, the most prominent scheme, the Lightning Network payment channel, requires participants to regularly monitor blockchain; otherwise, there is a potential risk of fund loss. Moreover, this scheme also fails to support participants in settling the final payment balances in real time, compromising the efficiency of fund utilization. Existing payment channel enhancing technologies are unable to overcome the above issues without compromising payment privacy. To solve the above issues, we apply the Intel Software Guard Extensions (SGX), which provides trusted execution environments with confidentiality and integrity guarantees, to design a novel Bitcoin payment channel scheme. The scheme can support real-time settlement yet guarantee the participants' fund security without monitoring the blockchain. Through a combination of the additive homomorphic property of keys, the secret sharing scheme, and customized punishments, our scheme can still guarantee fund security and off-chain transaction privacy, even if the confidentiality of SGX is compromised by side-channel attacks. Finally, security and performance analysis demonstrate that our scheme allows participants to construct a secure yet efficient payment channel to transfer value.

As a standard solution to the problem of private deep learning, differential privacy (DP) is widely used in graph neural networks (GNNs) to protect sensitive information about the input graph data. However, most existing DP algorithms for GNNs protect the privacy of every attribute for each node. This results in the need for injecting a large amount of noise, making these methods significantly underperform their non-private counterparts. We argue that in some practical scenarios, node labels serve as the only or the most sensitive attribute, where label differential privacy, a more fine-grained notion of differential privacy that only protects the labels is more appropriate. To better capture these scenarios and improve the trade-off between data privacy and model accuracy, we propose a novel method of training GNNs under label differential privacy. Instead of naively adding noise to the node labels before training the GNN, our method follows the strategy of Private Aggregation of Teacher Ensembles (PATE) to generate differentially private node labels with both high accuracy and strong privacy guarantee. We also propose a label denoising module that takes advantage of the graph structure to further improve the accuracy of the trained model. Additionally, our method is model-agnostic, making it applicable to any GNN architecture. We evaluate its performance on two commonly used benchmark datasets and demonstrate its capability to learn high-performance models while ensuring privacy.

Data sharing has led to the emergence of the deep generative model (DGM) with differential privacy for synthesizing tabular data. However, existing methods struggle to synthesize high-utility tabular data with enhanced privacy. One challenge is degraded data utility due to the limited number of training iterations available under strong privacy guarantees. The other challenge is that widely-used encoding schemes may leak the sensitive distribution of continuous features. To this end, we propose a novel pipeline incorporating split-and-discard pre-training and an embedding module to synthesize data. To reduce the impact of limited iterations, we employ the split-and-discard pre-training method. This method leverages the intrinsic structure of DGM, which can be split into discriminative and generative sub-models. By conducting pre-training and discarding specific sub-models of DGM on private data, we address these challenges while training models with differential privacy. To preserve the privacy of continuous features, we propose a piecewise linear one-hot encoding scheme followed by an embedding layer. We instantiate this pipeline using variational autoencoders and generative adversarial networks respectively and compare them against popular models and variants. Results show that our pipeline on private data effectively balances privacy and utility.

Blockchain is the most promising technology to tackle the security challenges of certificate revocation schemes, such as vulnerability to the single point of failure and lack of accountability systems. However, current blockchain-based certificate revocation systems suffer from privacy problems as the blockchain nodes can learn which website the client is going to connect with and infer the end-user's private information, such as identity, location, and health condition. In this paper, we propose a decentralized certificate revocation scheme that allows clients to securely and privately verify revocation information. We not only take advantage of blockchain to provide security guarantees but also further craft a novel multi-server offline/online private information retrieval (PIR) protocol named MOO-PIR to preserve query privacy for clients even if a subset of servers collude. Finally, we provide security analysis and performance evaluation, demonstrating that our scheme can protect client privacy without compromising efficiency.

Running a typical Bitcoin client (also called full node) needs more than 444 GB of disk space, considerable time, and computational resources to synchronize the entire blockchain, which is infeasible for resource-constrained devices. To address such concerns, the lightweight Bitcoin client proposed by Satoshi outsources most of computational and storage burdens to full nodes. Unfortunately, interacting with full nodes to query transactions leaks considerable information like addresses and transactions of lightweight client users. Thus, Bitcoin users that rely on lightweight clients are subject to de-anonymization, which defeats users privacy. Traditional schemes cannot support lightweight clients to query transactions from full nodes in an efficient yet privacy-preserving way. In this article, we propose a new efficient yet privacy-preserving transaction query scheme that specially targets the missing support for lightweight clients. We identify unique characteristics of the Bitcoin blockchain and craft a highly customized private information retrieval scheme called BIT-PIR to match the Bitcoin transaction query scenario and boost performances. Moreover, we customize a storage structure of the Bitcoin blockchain so that it further improves the query efficiency of our scheme. Finally, we develop a prototype implementation to demonstrate the feasibility of our proposed scheme.

The current privacy-preserving Graph Neural Networks (GNNs) cannot provide security and privacy guarantees against malicious adversaries without sacrificing accuracy and efficiency. For example, the Secure Multi-party Computation (MPC) can resist malicious adversaries while adding severe overhead. Trusted Execution Environment (TEE), such as Intel Software Guard Extension (SGX), can guarantee privacy and faithful execution without compromising efficiency. However, existing attacks can compromise the confidentiality of SGXs. Besides, the CPU-based structure of SGX restricts its extensibility that cannot perform collaborative computation with GPUs. To address the above issues, we propose a novel GNN training and inference framework to support data holders outsourcing their computation tasks to servers. First, we combine the advantage of MPC and the code integrity protection provided by SGXs to resist malicious adversaries without sacrificing efficiency. Second, we adopt a strategy that allows the servers to transfer the parallelizable computation task to the untrusted yet high-performance GPUs, further improving efficiency without hindering privacy. To the best of our knowledge, our proposal is the first privacy-preserving GNN framework against malicious adversaries without sacrificing accuracy and efficiency. Experiments on real-world citation datasets have demonstrated the performance of our framework regarding security, privacy, accuracy, and efficiency.

Mixing schemes have been applied by Bitcoin users to break their payment links in the blockchain to enhance privacy. However, most mixing schemes cannot provide secure mixing service without compromising efficiency since they are relying on complex cryptographic techniques or interactive protocols. To provide secure yet efficient mixing service, researchers introduce Intel SGX enclave, which provides Trusted Execution Environment (TEE) with confidentiality and integrity guarantees to execute mixing operations. Unfortunately, users will lose their mixing funds if a malicious service provider compromises the confidentiality guarantee of his/her enclave. Moreover, the scheme cannot scale to a large number of users in a single mixing round, that is, limited scalability. In this paper, we present a novel decentralized mixing scheme with multiple enclaves run by different service providers, which uses Shamir secret sharing scheme and additive homomorphic property of keys in Elliptic Curve Cryptography to tolerate a subset of enclaves to be compromised. Moreover, our scheme also provides stronger scalability so it achieves anonymity sets by orders of magnitude higher than the existing TEE-based mixing scheme. The experiment shows our scheme can provide stronger security and anonymity guarantees without compromising efficiency which outperforms existing mixing schemes.

For the future large-scale IoT ecosystem, the number and frequency of micro-payments will increase dramatically. However, the mainstream of cryptocurrencies such as Bitcoin and Ethereum fail to meet the need for a large-scale IoT ecosystem due to limit transaction throughput and high transaction fee. Although Layer-2 solutions such as Lightning Network (LN) increases the throughput of cryptocurrencies by allowing participants to conduct off-chain transactions, LN still suffers from two main limitations: participants need to access the Blockchain within a short bounded time, and a payment channel can only accommodate two participants. To overcome these limitations, we propose HyperChannel, a novel distributed layer-2 payment network designed specifically for the IoT ecosystem which outsources the transaction processing task safely to a group of Intel Software Guard Extensions (SGXs) run by for-profit selfish third parties. Clients such as IoT devices and IoT service providers who often trade with each other will be assigned to a channel to conduct high-frequency in-channel transactions while being allowed to conduct crosschannel transactions in a fee-saving fashion. Compared with existing SGX-based layer-2 payment framework, HyperChannel achieves maximum throughput, addresses both limitations of LN, and further lightens the burden of participants so that IoT devices can conduct layer-2 transactions without running an SGX by themselves.

Hardware trusted execution environment (TEE) provided by Intel SGX enclave has been introduced in existing payment channel schemes as a root-of-trust to enforce faithful protocol execution so that participants do not need to monitor Bitcoin blockchain anymore. However, the security of these schemes relies totally on enclaves. Since private keys of all channel funds are kept by both payment channel participants’ enclaves, a malicious participant can steal funds from the counterparty by defeating her own enclave. To solve the above problem, we present a novel TEE-based payment channel scheme that transfers the responsibility of running enclaves from participants to a third party committee, while relieving both participants from monitoring the blockchain at the same time. Furthermore, since committee members can try to steal funds by defeating their own enclaves, we exploit the additive homomorphic property of signature keys in Elliptic Curve Cryptography to design a novel secret sharing scheme to tolerate a subset of committee members to be malicious. By using the above secret sharing scheme, private keys of the channel funds are never constructed in any committee member’s enclave, so that a malicious committee member cannot steal funds by defeating his own enclave. Finally, experiment shows our scheme can ensure payment channel funds security without efficient compromises compared with existing TEE-based payment channel schemes.

In Bitcoin, lightweight clients outsource most of storage and computation tasks to full nodes in order to run on resource-limited devices. In the interaction with the full node, the lightweight client leaks considerable information about which address or transaction is relevant to it. The existing schemes to solve this problem do not support efficient yet privacy-preserving transaction search due to the fact that the blockchain is inherently inefficient for transaction query and proposed schemes perform transaction search in a block-by-block manner. Therefore, we propose an efficient transaction query scheme for the privacy-preserving lightweight client with the Intel SGX enclave running on the full node. Our main idea is to leverage the secure enclave to serve transaction-query requests from lightweight clients. However, the usage of secure enclave alone does not achieve our goals. Our scheme reorganizes the blockchain and leverages prefix tree to increase transaction-search efficiency. Due to limited capacity, the secure enclave stores reorganized blockchain data in the untrusted full node. Thus, our scheme integrates prefix tree and oblivious searching technologies to simultaneously support efficient transaction search and protect access pattern of externally stored blockchain data for the secure enclave. Security analysis and performance evaluation show that our scheme provides efficient transaction search and verification functionalities for lightweight Bitcoin clients in a privacy-preserving way.