ACM Transactions on Database Systems最新文献

Many transactions in web applications are constructed ad hoc in the application code. For example, developers might explicitly use locking primitives or validation procedures to coordinate critical code fragments. We refer to database operations coordinated by application code as ad hoc transactions. Until now, little is known about them. This paper presents the first comprehensive study on ad hoc transactions. By studying 91 ad hoc transactions among 8 popular open-source web applications, we found that (i) every studied application uses ad hoc transactions (up to 16 per application), 71 of which play critical roles; (ii) compared with database transactions, concurrency control of ad hoc transactions is much more flexible; (iii) ad hoc transactions are error-prone—53 of them have correctness issues, and 33 of them are confirmed by developers; and (iv) ad hoc transactions have the potential for improving performance in contentious workloads by utilizing application semantics such as access patterns. Based on these findings, we discuss the implications of ad hoc transactions to the database research community.

Hardware-enabled enclaves have been applied to efficiently enforce data security and privacy protection in cloud database services. Such enclaved systems, however, are reported to suffer from I/O-size (also referred to as communication-volume)-based side-channel attacks. Albeit differentially private padding has been exploited to defend against these attacks as a principle method, it introduces a challenging bi-objective parametric query optimization (BPQO) problem and current solutions are still not satisfactory. Concretely, the goal in BPQO is to find a Pareto-optimal plan that makes a tradeoff between query performance and privacy loss; existing solutions are subjected to poor computational efficiency and high cloud resource waste. In this article, we propose a two-phase optimization algorithm called TPOA to solve the BPQO problem. TPOA incorporates two novel ideas: divide-and-conquer to separately handle parameters according to their types in optimization for dimensionality reduction; on-demand-optimization to progressively build a set of necessary Pareto-optimal plans instead of seeking a complete set for saving resources. Besides, we introduce an acceleration mechanism in TPOA to improve its efficiency, which prunes the non-optimal candidate plans in advance. We theoretically prove the correctness of TPOA, numerically analyze its complexity, and formally give an end-to-end privacy analysis. Through a comprehensive evaluation on its efficiency by running baseline algorithms over synthetic and test-bed benchmarks, we can conclude that TPOA outperforms all benchmarked methods with an overall efficiency improvement of roughly two orders of magnitude; moreover, the acceleration mechanism speeds up TPOA by 10-200×.

Constraint satisfaction problems (CSP’s) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSP’s and computation of zeroth frequency moments (F₀) for data streams.

Our investigations lead us to observe a striking similarity in the core techniques employed in the algorithmic frameworks that have evolved separately for model counting and F₀ computation. We design a recipe for translating algorithms developed for F₀ estimation to model counting, resulting in new algorithms for model counting. We also provide a recipe for transforming sampling algorithm over streams to constraint sampling algorithms. We then observe that algorithms in the context of distributed streaming can be transformed into distributed algorithms for model counting. We next turn our attention to viewing streaming from the lens of counting and show that framing F₀ estimation as a special case of #DNF counting allows us to obtain a general recipe for a rich class of streaming problems, which had been subjected to case-specific analysis in prior works. In particular, our view yields an algorithm for multidimensional range efficient F₀ estimation with a simpler analysis.

Data-intensive applications have fueled the evolution of log-structured merge (LSM) based key-value engines that employ the out-of-place paradigm to support high ingestion rates with low read/write interference. These benefits, however, come at the cost of treating deletes as second-class citizens. A delete operation inserts a tombstone that invalidates older instances of the deleted key. State-of-the-art LSM-engines do not provide guarantees as to how fast a tombstone will propagate to persist the deletion. Further, LSM-engines only support deletion on the sort key. To delete on another attribute (e.g., timestamp), the entire tree is read and re-written, leading to undesired latency spikes and increasing the overall operational cost of a database. Efficient and persistent deletion is key to support: (i) streaming systems operating on a window of data, (ii) privacy with latency guarantees on data deletion, and (iii) en masse cloud deployment of data systems.

Further, we document that LSM-based key-value engines perform suboptimally in presence of deletes in a workload. Tombstone-driven logical deletes, by design, are unable to purge the deleted entries in a timely manner, and retaining the invalidated entries perpetually affects the overall performance of LSM-engines in terms of space amplification, write amplification, and read performance. Moreover, the potentially unbounded latency for persistent deletes brings in critical privacy concerns in light of the data privacy protection regulations, such as the right to be forgotten in EU’s GDPR, the right to delete in California’s CCPA and CPRA, and deletion right in Virginia’s VCDPA. Toward this, we introduce the delete design space for LSM-trees and highlight the performance implications of the different classes of delete operations.

To address these challenges, in this article, we build a new key-value storage engine, Lethe⁺, that uses a very small amount of additional metadata, a set of new delete-aware compaction policies, and a new physical data layout that weaves the sort and the delete key order. We show that Lethe⁺ supports any user-defined threshold for the delete persistence latency offering higher read throughput (1.17 × −1.4 ×) and lower space amplification (2.1 × −9.8 ×), with a modest increase in write amplification (between (4% ) and (25% )) that can be further amortized to less than (1% ). In addition, Lethe⁺ supports efficient range deletes on a secondary delete key by dropping entire data pages without sacrificing read performance or employing a costly full tree merge.

In the era of big data, data sharing not only boosts the economy of the world but also brings about problems of privacy disclosure and copyright infringement. The collected data may contain users’ sensitive information; thus, privacy protection should be applied to the data prior to them being shared. Moreover, the shared data may be re-shared to third parties without the consent or awareness of the original data providers. Therefore, there is an urgent need for copyright tracking. There are few works satisfying the requirements of both privacy protection and copyright tracking. The main challenge is how to protect the shared data and realize copyright tracking while not undermining the utility of the data. In this article, we propose a novel solution of a reversible database watermarking scheme based on order-preserving encryption. First, we encrypt the data using order-preserving encryption and adjust an encryption parameter within an appropriate interval to generate a ciphertext with redundant space. Then, we leverage the redundant space to embed robust reversible watermarking. We adopt grouping and K-means to improve the embedding capacity and the robustness of the watermark. Formal theoretical analysis proves that the proposed scheme guarantees correctness and security. Results of extensive experiments show that OPEW has 100% data utility, and the robustness and efficiency of OPEW are better than existing works.