The query optimizer is a fundamental component of database management systems that determines the most efficient execution strategy for a given query by evaluating alternative query plans. Among its tasks, join optimization plays a central role, as the order of joins in multi-table queries can significantly affect execution performance. However, due to the inherent complexity of join optimization, logical bugs are inevitable and often difficult to detect. While existing fuzzing tools have shown notable success in uncovering crash- and performance-related errors, effectively identifying logical bugs -- cases in which the system produces incorrect query results -- remains largely unresolved. In this paper, we propose a metamorphic testing approach to detect DBMS bugs related to INNER JOIN optimization through the lens of set theory. For each testing case, equivalent queries are generated based on a basic set operation -- intersection -- and three semantics-preserving transformation rules, i.e., symmetric join transformation, asymmetric difference transformation, and symmetric difference transformation, are introduced. These rules rewrite a simple NATURAL/INNER JOIN query into a more complex, yet semantically equivalent, form. We implement this design in JoinEquiv, which serves as a testing oracle to systematically uncover logical inconsistencies in DBMS query processing by comparing the results of original and transformed queries. Using JoinEquiv, we uncovered 29 previously unknown issues in mainstream DBMSs (MySQL, TiDB, DuckDB, and Percona), and 27 of them were officially confirmed. JoinEquiv reveals deep logical flaws in DBMS optimizers and executors, underscoring its value in enhancing DBMS robustness.

This paper investigates the feasibility of achieving zero-knowledge verifiability for graph databases, enabling database owners to cryptographically prove the query execution correctness without disclosing the underlying data. Although similar capabilities have been explored for relational databases, their implementation for graph databases presents unique challenges. This is mainly attributed to the relatively large complexity of queries in graph databases. When translating graph queries into arithmetic circuits, the circuit scale can be too large to be practically evaluated. To address this issue, we propose to break down graph queries into more fine-grained, primitive operators, enabling a step-by-step evaluation through smaller-scale circuits. Accordingly, the verification with ZKP circuits of complex graph queries can be decomposed into a series of composable cryptographic primitives, each designed to verify a fundamental structural property such as path ordering or edge directionality. Especially, having noticed that the graph expansion (i.e., traversing from nodes to their neighbors along edges) operation serves as the backbone of graph query evaluation, we design the expansion centric operator decomposition. In addition to constructing circuits for the expansion primitives, we also design specialized ZKP circuits for the various attributes that augment this traversal. The circuits are meticulously designed to take advantage of PLONKish arithmetization. By integrating these optimized circuits, we implement ZKGraph, a system that provides verifiable query processing while preserving data privacy. Performance evaluation indicates that ZKGraph significantly outperforms naive in circuit implementations of graph operators, achieving substantial improvements in both runtime and memory consumption.

Various real-world graphs, such as social and transaction networks, are typically distributed across users, each of whom holds a local view of the graph (i.e., their own relationships with others). Densest Subgraph Discovery (DSD) on such decentralized graphs is a fundamental task that can uncover valuable insights for downstream applications, including fraud detection, community identification, and user behavior mining. Additionally, in many scenarios, due to privacy concerns, sensitive original local views cannot be collected for DSD. Although there have been extensive studies on DSD, most existing algorithms either do not take user privacy into account or are specific to the centralized privacy setting that requires a (trusted) curator to collect all local views from users and then analyze the entire graph privately.

Inverted index is a basic tool in many data-intensive applications. Though numerous efforts have been made on efficient inverted index-based query processing, existing schemes do not achieve the expected performance for modern data centers, in which servers are equipped with powerful CPUs and relatively large memory. Through comprehensive measurement studies, we identify the root course is that the data formats for index representation make it unfeasible to design efficient query execution approaches on top of it, which results in poor parallel query support and waste CPU computation. Driven by the findings, we propose to reconcile the in-memory index as columnar structures. To enable this idea, we construct the compact columnar format (i.e., Cocoa) that achieves both desirable space efficiency and maintains the capability for efficient searching support. With Cocoa, we design an efficient query executing scheme that utilizes vectorized batch processing to avoid frequent branch prediction, as well as clause enumeration with pruning to save the overhead of intermediate batch materialization. We build an open-source system VeloSearch to embody our design; experimental results show that VeloSearch achieves ~30× better performance compared with state-of-the-art search libraries such as Lucene and Tantivy.

Portfolio optimization encounters persistent challenges in adapting to dynamic markets due to static assumptions and high-dimensional decision spaces. Although reinforcement learning (RL) has emerged as a potential solution, conventional reward engineering often fails to capture complex market dynamics. Recent advances in deep RL and graph neural networks have attempted to enhance market microstructure modeling. However, these methods still struggle with the systematic integration of financial knowledge. To address the above issues, we propose a novel heuristic-guided inverse reinforcement learning framework for portfolio optimization. Specifically, our framework provides an interpretable expert strategy generation mechanism that takes into account sector diversification and correlation constraints. Then, a multi-objective reward optimization method is adopted to adaptively strike a balance between returns and risks. Furthermore, it also utilizes heterogeneous graph policy learning with hierarchical attention mechanisms to explicitly model inter-stock relationships. Finally, we conduct extensive experiments on real-world financial market data to demonstrate that our framework outperforms several state-of-the-art deep learning and RL baselines in terms of risk-adjusted returns. We provide case studies to showcase the ability of our framework to balance return maximization and risk containment. Our code is publicly available at https://github.com/ChloeWenyiZhang/SmartFolio/.

Database connectors are critical components that enable applications to interact with database management systems (DBMS) but their security vulnerabilities are often neglected. Unlike traditional software defects, connector vulnerabilities exhibit subtle behavioral patterns and are inherently challenging to detect. Moreover, non-standardized implementation of connectors leaves potential risks (i.e., unsafe implementations) but is more elusive. As a result, existing fuzzing methods are ineffective in finding such vulnerabilities. Even large language model (LLM)-based methods are still incapable of generating test cases that can invoke all the interface and internal logic of database connectors due to a lack of domain knowledge.In this paper, we propose a new LLM-based test case generation method guided by reinforcement learning (RL) for database connector testing. Specifically, to equip the LLM with sufficient and appropriate domain knowledge, a parameterized template is composed for prompt construction. The LLM then generates test cases instructed by the constructed prompts, which are dynamically evaluated through differential testing across multiple connectors. The testing process is carried out iteratively, where RL is adopted to select the optimal prompt in each round based on behavioral feedback from the previous rounds, to maximize the efficiency of discovering inconsistencies. Finally, we implement and evaluate the aforementioned methodology on two widely used JDBC connectors, namely MySQL Connector/J and OceanBase Connector/J. In the preliminary results, we have reported 16 bugs, among which 10 are officially confirmed, and the rest are acknowledged as unsafe implementations.

Abstract Distributed in-memory databases are widely adopted to achieve low latency and high bandwidth for data-intensive applications. They support scale-out by sharding and distributing data across multiple nodes. To efficiently adapt to various workloads, distributed in-memory databases must be capable of migrating shards across nodes. In this paper, we demonstrate that state-of-the-art approaches experience significant performance degradation during migration due to service downtime and redundant data transfer. Furthermore, our findings indicate that the presence of service downtime constrains the scalability of migration strategies, while the transfer of redundant data during the snapshot transfer phase limits their adaptability to dynamic workloads. To this end, this paper proposes Aion, a live migration strategy designed for distributed in-memory databases. Aion eliminates any potential service downtime by immediately switching transaction routing to the destination node. To ensure data consistency between the source and destination nodes, as well as serializable execution during migration, Aion proposes the mutual validation phase. Moreover, Aion introduces an analysis phase before the snapshot transfer phase to identify dynamically changing hotspots in workloads. The analysis phase identifies and transfers tuples and versions accessed less frequently to the destination node, reducing the amount of data transferred. Aion is implemented on a distributed in-memory database and evaluated using various OLTP workloads. The results demonstrate that Aion can fundamentally eliminate service downtime, adapt effectively to various workloads and exhibit robust scalability. Compared to state-of-the-art approaches, Aion achieves up to 2.25x–6.57x higher throughput during migration and shortens the migration duration by 53.7–68.2%.

Fairness in information access on social networks has been actively investigated in recent years. Most existing studies on this topic focus on the problem of Fair Influence Maximization (FIM), which aims to select a set of seed nodes such that a propagation campaign initiated by them has fair influence spread across different groups. Although FIM approaches can guarantee fair access to specific information, they cannot resolve the inherent disparities in information access between different groups arising from graph structures. To address this issue, we study the problem of augmenting the graph structure via edge suggestion towards fairer information access at the group level. Specifically, we formulate a new optimization problem called Fair Information Access via Edge Suggestion (FIAES) that identifies a set of at most b non-existing edges to be added into the graph such that they not only maximally increase the total influence spread, but also ensure fairness in the sense that the influence spreads within different groups are proportional to their population sizes . Since FIAES is NP-hard and cannot be approximated within any constant factor, unless P = NP , we propose FIAES-RL, a reinforcement learning-based algorithm for edge selection that strikes a balance between influence and fairness objectives. Finally, with extensive experimentation on four synthetic and real-world networks, we demonstrate that FIAES-RL outperforms several state-of-the-art baseline methods for fairness-aware edge suggestion, reducing inequity in information access while significantly boosting information propagation.

Memory disaggregation has emerged as a promising architecture for improving resource efficiency by decoupling the computing and memory resources. But building efficient range indices in such an architecture faces three critical challenges: (1) coarse-grained concurrency control schemes for coordinating concurrent read/write operations with node splitting incur high contention under the skewed and write-intensive workloads; (2) existing data layouts fail to balance consistency verification and hardware acceleration via SIMD (Single Instruction Multiple Data); and (3) naive caching schemes struggle to adapt to rapidly changing access patterns. To address these challenges, we proposeMariana, a memory-disaggregated skiplist index that integrates three key innovations. First, it uses a fine-grained (i.e., entry-level) latch mechanism combined with dynamic node resizing to minimize the contention and splitting frequency. Second, it employs a tailored data layout for leaf node, which separates keys and values to enable SIMD acceleration while maintaining consistency checks with minimal write overhead. Third, it implements an adaptive caching strategy that tracks node popularity in real-time to optimize network bandwidth utilization during the index traversal. Experimental results show thatMarianaachieves$1.7\times$higher throughput under write-intensive workloads and reduces the P90 latency by 23% under the read-intensive workloads, when comparing to the state-of-the-art indices on disaggregated memory.

Payment channel networks (PCNs) are generally regarded as one of the most effective and promising scalability solutions for blockchain-based cryptocurrency systems, but suffer the issues of low success ratio and long confirmation latency in processing transactions. In this paper, we demonstrate the feasibility of tremendously increasing the success ratio of transactions and improving their execution efficiency by enhancing network nodes' connectivity and enforcing a balanced network channel capacity. To implement such ideas, multiple designs have been made. First, to extent nodes connectivity, we transform the nearly-linear ordered nodes into a star payment structured typology, and design an incentive financing mechanism to restructure a new landmark routing typology design. Especially, for the marginalized or dissociative nodes, we utilize specific financial loan strategies to encourage them to (re)join the system. Besides, we propose the Power Atomic Multi-Path Payments (Power AMP) traffic distribution method, which hierarchically allocates the bottleneck's currently-available capacity (to replace the random or equal division used in traditional AMP), and thus archives a balanced traffic usage. With such efforts, we improve the transaction success ratio and efficiency of transaction exertion by order of magnitude—compared with traditional PCN using the benchmark of landmark route, our method improves the success ratio by 11.06%.

Machine unlearning is the process through which a deployed machine learning model is enforced to forget about some of its training data items. It normally generates two machine learning models, the original model and the unlearned model, indicating training results before and after data items are deleted. However, recent studies find that machine unlearning is vulnerable to membership inference attacks—as the directivity of training and nontraining data (i.e., data items in the training set have high posterior probabilities), the attackers can utilize this property to infer whether an item has been used for original model training. Nevertheless, such attacks are incapable in label-only settings, in which the attackers are infeasible to get the posteriors. In this paper, we propose a new label-only membership inference attack scheme targeted at machine unlearning to eliminate the dependence on posteriors. Our heuristic is that injected turbulence on candidate samples will present different behaviors for training and nontraining data. Thus, in our scheme, the attacker iteratively query on the original/unlearned models and inject turbulence to change their predicting labels; it determines whether an item is having-been-delated by observing the disturbance amplitude. Extensive experiments (i.e., on MNIST, CIFAR10, CIFAR100, and STL10 data sets) show that our method achieves high inference accuracy (measured by AUC) in label-only settings, for example, AUC = 0.96 for MNIST data set. Besides, we analyze the existing countermeasures in mitigating inference attacks and find that our scheme can bypass most of them.

This article studies how today's cloud storage services support collaborative file editing. As a tradeoff for transparency and user-friendliness, they do not ask collaborators to use version control systems but instead implement their own heuristics for handling conflicts, which however often lead to unexpected and undesired experiences. With specialized measurements and reverse engineering, we unravel a number of their design and implementation issues as the root causes of poor experiences. Driven by the findings, we propose to reconsider the collaboration support of cloud storage services from a novel perspective ofoperationswithout using any locks. To enable this idea, we design intelligent and efficient approaches to the inference and transformation of users’ editing operations, as well as optimizations to the maintenance of files’ historical versions and the update of individual files. We build an open-source system UFC2 (User-Friendly Collaborative Cloud) to embody our design, which can avoid most (98%) conflicts with little (2%) overhead.

Cloud storage services (e.g., Dropbox) have become pervasive in not only simple file sharing but also advanced collaborative file editing (collaboration for short). Using Dropbox for collaboration is much easier than SVN and Git, thus greatly facilitating common users. In practice, however, many Dropbox users are perplexed by unexpected collaboration conflicts, which severely impair their experiences. Through various benchmark experiments, we unveil the two root causes of collaboration conflicts: 1) Dropbox never locks an edited file during collaboration; 2) Dropbox only guarantees eventual data consistency among the collaborators, significantly aggravating the probability of conflicts. In this paper, we attempt to enable conflict-free collaborations with Dropbox-like cloud storage services. This attempt is empowered by three key findings and measures. First, although the end-to-end sync delay is unpredictable due to eventual consistency, we can always track the latest version of an edited file by actively resorting to the cloud via certain web APIs. Second, although all application-level data is encrypted in Dropbox, we can roughly deduce the sync status from traffic statistics. Third, applying a couple of useful mechanisms (e.g., distributed architecture and data lock) learned from Git, we can effectively and efficiently avoid collaboration conflicts-of course, this requires re-implementing Git mechanisms in cloud storage services with minimum overhead and user interference. Integrating above efforts, we build the ConflictReaper system capable of helping users automatically avoid almost all collaboration conflicts with affordable network and computation overhead.

Blockchain has been an emerging technology, which comprises lots of fields such as distributed systems and Internet of Things (IoT). As is well known, blockchain is the underlying technology of bitcoin, whose initial motivation is derived from economic incentives. Therefore, lots of components of blockchain (e.g., consensus mechanism) can be constructed toward the view of game theory. In this paper, we highlight the combination of game theory and blockchain, including rational smart contracts, game theoretic attacks, and rational mining strategies. When put differently, the rational parties, who manage to maximize their utilities, involved in blockchain chose their strategies according to the economic incentives. Consequently, we focus on the influence of rational parties with respect to building blocks. More specifically, we investigate the research progress from the aspects of smart contract, rational attacks, and consensus mechanism, respectively. Finally, we present some future directions based on the brief survey with respect to game theory and blockchain.

Recently, several practical attacks raised serious concerns over the security of searchable encryption. The attacks have brought emphasis on forward privacy, which is the key concept behind solutions to the adaptive leakage-exploiting attacks, and will very likely to become a must-have property of all new searchable encryption schemes. For a long time, forward privacy implies inefficiency and thus most existing searchable encryption schemes do not support it. Very recently, Bost (CCS 2016) showed that forward privacy can be obtained without inducing a large communication overhead. However, Bost's scheme is constructed with a relatively inefficient public key cryptographic primitive, and has poor I/O performance. Both of the deficiencies significantly hinder the practical efficiency of the scheme, and prevent it from scaling to large data settings. To address the problems, we first present FAST, which achieves forward privacy and the same communication efficiency as Bost's scheme, but uses only symmetric cryptographic primitives. We then present FASTIO, which retains all good properties of FAST, and further improves I/O efficiency. We implemented the two schemes and compared their performance with Bost's scheme. The experiment results show that both our schemes are highly efficient.

With the explosive growth of Internet traffic, multi-source content delivery has been introduced for improving the performance and quality-of-experience (QoE) of Internet services. Upgrading from single-source content delivery to multi-source content delivery, however, may not always lead to a better performance. Instead, a decline in terms of delivery speed often occurs. By conducting a comprehensive study, we show that the underlying reason of this counter-intuitive phenomenon is actually due to the cask effect of data sources at both macro and micro level. Specifically, at the macro level, data sources with different types are highly heterogeneous in terms of delivery performance, which means data sources with certain types are particularly easy to become the "short boards". At the micro level, for the data sources chosen by a client, the high diversity of participation time (DPT) of the sources could impair the acceleration effect. Motivated by the above findings, we design MDR (Multi-source Delivery Redirector), a middleware that contains two optimizations to improve the acceleration effect. One is the feature-greedy selection algorithm which can avoid selecting data sources with inferior types, and the other is the DPT-driven shuffle strategy which can avoid using unstable data sources. Simulation-based experiments show that the MDR outperforms existing approaches in terms of overall downloading performance.

Object storage clouds (e.g., Amazon S3) have become extremely popular due to their highly usable interface and cost-effectiveness. They are, therefore, widely used by various applications (e.g., Dropbox) to host user data. However, because object storage clouds are flat and lack the concept of a directory, it becomes necessary to maintain file meta-data and directory structure in a separate index cloud. This paper investigates the possibility of using a single object storage cloud to efficiently host the whole filesystem for users, including both the file content and directories, while avoiding meta-data loss caused by index cloud failures. We design a novel data structure, Hierarchical Hash (or H2), to natively enable the efficient mapping from filesystem operations to object-level operations. Based on H2, we implement a prototype system, H2Cloud, that can maintain large filesystems of users in an object storage cloud and support fast directory operations. Both theoretical analysis and real-world experiments confirm the efficacy of our solution: H2Cloud achieves faster directory operations than OpenStack Swift by orders of magnitude, and has similar performance to Dropbox but yet does not need a separate index cloud.

This paper reveals the performance anomaly (i.e., the decline of delivery speed) when the client upgrades a task from single-source content delivery to multi-source content delivery. This anomaly is mainly caused by two aspects: (1) data sources with different types vary greatly in terms of acceleration reward (AR), and data sources with certain types are particularly easy to become inferior; (2) When the data sources remain fixed for a period of time, the large diversity of participant time (DPT) of data sources disturb the acceleration and the data sources with less participant time are inferior. Combing these insights, we figure out that the multi-source content delivery is limited by the so-called cask effect, i.e., the acceleration effect mainly depends on the inferior data sources.