With the rapid advancement of the digital economy, data collaboration between organizations has become a well-established business model, driving the growth of various industries. However, privacy concerns make direct data sharing impractical. To address this, Two-Party Split Learning (a.k.a. Vertical Federated Learning (VFL)) has emerged as a promising solution for secure collaborative learning. Despite its advantages, this architecture still suffers from low computational resource utilization and training efficiency. Specifically, its synchronous dependency design increases training latency, while resource and data heterogeneity among participants further hinder efficient computation. To overcome these challenges, we propose \texttt{PubSub-VFL}, a novel VFL paradigm with a Publisher/Subscriber architecture optimized for two-party collaborative learning with high computational efficiency. \texttt{PubSub-VFL} leverages the decoupling capabilities of the Pub/Sub architecture and the data parallelism of the parameter server architecture to design a hierarchical asynchronous mechanism, reducing training latency and improving system efficiency. Additionally, to mitigate the training imbalance caused by resource and data heterogeneity, we formalize an optimization problem based on participants’ system profiles, enabling the selection of optimal hyperparameters while preserving privacy. We conduct a theoretical analysis to demonstrate that \texttt{PubSub-VFL} achieves stable convergence and is compatible with security protocols such as differential privacy. Extensive case studies on five benchmark datasets further validate its effectiveness, showing that \texttt{PubSub-VFL} compared to state-of-the-art baselines not only accelerates training by $2 \sim 7\times$ without compromising accuracy but also achieves computational resource utilization by up to 91.07\%.

Multi-party computation (MPC) has gained increasing attention in both research and industry, with many protocols adopting the preprocessing model to optimize online performance through the strategic use of offline-generated, data-independent correlated randomness (or correlation). However, while extensive research has been dedicated to enhancing the online phase, the equally critical offline phase remains largely overlooked. This gap imposes significant yet unaddressed challenges in both security and efficiency, hindering the practical adoption of MPC systems. To address these challenges, we build upon the pseudorandom correlation generator (PCG) concept by Boyle et al. (CRYPTO’19, FOCS’20) and propose HPCG, a programmable, verifiable, and concretely efficient PCG construction using small security hardware. Our core technique, termed verifiable silent preprocessing, enables virtually unbounded, on-demand generation of diverse correlated randomness with provable correctness while effectively reducing offline overhead in a correlation-agnostic manner. To demonstrate the benefits of our approach, we experimentally evaluate HPCG and compare it with other preprocessing techniques. We also show how HPCG can further optimize specialized secure computation tasks (e.g., shuffling and equality test) by promoting new, customized correlations, which may be of new interest.

Numerous studies have underscored the significant privacy risks associated with various leakage patterns in encrypted data stores. While many solutions have been proposed to mitigate these leakages, they either (1) incur substantial overheads, (2) focus on specific subsets of leakage patterns, or (3) apply the same security notion across various workloads, thereby impeding the attainment of fine-tuned privacy-efficiency trade-offs. In light of various detrimental leakage patterns, this paper starts with an investigation into which specific leakage patterns require our focus in the contexts of key-value, range-query, and dynamic workloads, respectively. Subsequently, we introduce new security notions tailored to the specific privacy requirements of these workloads. Accordingly, we propose and instantiate Swat, an efficient construction that progressively enables these workloads, while provably mitigating system-wide leakage via a suite of algorithms with tunable privacy-efficiency trade-offs. We conducted extensive experiments and compiled a detailed result analysis, showing the efficiency of our solution. Swat is about an order of magnitude slower than an encryption-only data store that reveals various leakage patterns and is two orders of magnitude faster than a trivial zero-leakage solution. Meanwhile, the performance of Swat remains highly competitive compared to other designs that mitigate specific types of leakage.

Dynamic searchable symmetric encryption (DSSE) enables a server to efficiently search and update over encrypted files. To minimize the leakage during updates, a security notion named forward and backward privacy is expected for newly proposed DSSE schemes. Those schemes are generally constructed in a way to break the linkability across search and update queries to a given keyword. However, it remains underexplored whether forward and backward private DSSE is resilient against practical leakage-abuse attacks (LAAs), where an attacker attempts to recover query keywords from the leakage passively collected during queries.

Decentralized storage (DS) projects such as Filecoin are gaining traction. Their openness mandates effective auditing mechanisms to assure users that their data remains intact. A blockchain is typically employed here as an unbiased public auditor. While the case for static data is relatively easy to handle, on-chain auditing of dynamic data with practical performance guarantees is still an open problem. Dynamic Proof-of-Storage (PoS) schemes developed for conventional cloud storage are not applicable to DS, since they require large storage proofs and/or large auditor states that are unmanageable by a resource-constrained blockchain. To fill the gap, we propose a family of dynamic on-chain auditing protocols that can produce concretely small auditor states while retaining the compact proofs promised by static PoS schemes. Our design revolves around a set of succinct data structures and optimization techniques for index information management. With proper instantiation and realistic parameters, our protocols can achieve 0.25MB on-chain state and 1.2KB storage proof for the auditing of 1TB data, outperforming previous dynamic PoS schemes that are adaptable for DS by orders of magnitude. As another practical contribution, we introduce a data abstraction layer that allows one to deploy the auditing protocols on arbitrary storage systems hosting dynamic data.

We introduce the problem of maximizing approximately $k$-submodular functions subject to size constraints. In this problem, one seeks to select $k$-disjoint subsets of a ground set with bounded total size or individual sizes, and maximum utility, given by a function that is "close" to being $k$-submodular. The problem finds applications in tasks such as sensor placement, where one wishes to install $k$ types of sensors whose measurements are noisy, and influence maximization, where one seeks to advertise $k$ topics to users of a social network whose level of influence is uncertain. To deal with the problem, we first provide two natural definitions for approximately $k$-submodular functions and establish a hierarchical relationship between them. Next, we show that simple greedy algorithms offer approximation guarantees for different types of size constraints. Last, we demonstrate experimentally that the greedy algorithms are effective in sensor placement and influence maximization problems.