With the escalation in the demand for privacy-preserving and tamper-resistant data management and processing on the public cloud, an increasing number of mainstream databases start to provide always-encrypted and blockchain-like features, including Microsoft SQL Server, MongoDB, and Alibaba PolarDB. The recent progress in Trusted Execution Environment (TEE) technology has enabled the deployment of the complete database engine within TEE. This implementation ensures that data stored in memory, cache, and registers is encrypted, thereby maintaining the confidentiality of information. In this paper, we present SecuDB, a multi-granularity privacy-preserving and tamper-resistant relational database by placing the entire RDBMS in Intel TDX. We propose a novel visibility control mechanism incorporating column masking, log masking, and statistics masking to realize fine-grained privacy preservation and devise an isolated TEE-endorsed temporal table method to support efficient data and query verifiability, without affecting insertion and selection performance. We evaluate SecuDB using Sysbench, TPC-C and TikTok copyright workloads. The results show that compared with a system without an enclave, SecuDB hits 84.7% and 94.7% of the performance when providing coarse-grained and fine-grained privacy preservation, respectively. While the overhead for tamper-resistance is less than 22.6%.

Blockchain-like ledger databases emerge in recent years as a more efficient alternative to permissioned blockchains. Conventional ledger databases mostly rely on authenticated structures such as the Merkle tree and transparency logs for supporting auditability, and hence they suffer from the performance problem. As opposed to conventional ledger DBMSes, we design VeDB - a high-performance verifiable software (Ve-S) and hardware (Ve-H) enabled DBMS with rigorous auditability for better user options and broad applications. In Ve-S, we devise a novel verifiable Shrubs array (VSA) with two-layer ordinals (serial numbers) which outperforms conventional Merkle tree-based models due to lower CPU and I/O cost. It enables rigorous auditability through its efficient credible timestamp range authentication method, and fine-grained data verification at the client side, which are lacking in state-of-the-art relational ledger databases. In Ve-H, we devise a non-intrusive trusted affiliation by TEE leveraging digest signing, monotonic counters, and trusted timestamps in VeDB, which supports both data notarization and lineage applications. The experimental results show that VeDB-VSA outperforms Merkle tree-based authenticated data structures (ADS) up to 70× and 3.7× for insertion and verification; and VeDB Ve-H data lineage verification is 8.5× faster than Ve-S.

Verifiability is the backbone of most ledger systems to realize credible authentication. However, existing permissioned blockchains and centralized ledger databases lack rigorous verifiability to authenticate all facts (i.e., what-when-who validation). Besides, they suffer from high verification cost to a continually growing immutable storage. In this paper, we introduce verification principles behind LedgerDB, a centralized ledger database that achieves both strong external auditability and fast verification. We coin a novel concept called Dasein Verification that composes of three validation factors what-when-who to formalize ledger auditing. Regarding what, LedgerDB devises fam (fractal accumulating model) to accelerate existence verification, and CM-Tree for efficient lineage verification. Veri-fiable data mutations are also supported. For when, we discuss attacks on existing time pegging protocols that compromise the authenticity of timestamps, and propose a time notary protocol to resolve those threats. Evaluations show that fam and CM- Tree significantly outperform traditional approaches. Compared to Hyperledger Fabric, LedgerDB achieves 23x higher verification throughput with 500 x lower latency in notarization applications, and 3 x higher throughput with 300 x lower latency in lineage tracking applications. As a public-cloud ledger service, the end-to-end verification latencies of LedgerDB are on average 50 x and 1000x lower than that of QLDB in the above applications, respectively.

Developers often need to use appropriate APIs to program efficiently, but it is usually a difficult task to identify the exact one they need from a vast list of candidates. To ease the burden, a multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we propose a framework, BRAID (BoostingRecommendAtion withImplicit FeeDback), which leverages learning-to-rank and active learning techniques to boost recommendation performance. By exploiting user feedback information, we train a learning-to-rank model to re-rank the recommendation results. In addition, we speed up the feedback learning process with active learning. Existing query-based API recommendation approaches can be plugged into BRAID. We select three state-of-the-art API recommendation approaches as baselines to demonstrate the performance enhancement of BRAID measured by Hit@k (Top-k), MAP, and MRR. Empirical experiments show that, with acceptable overheads, the recommendation performance improves steadily and substantially with the increasing percentage of feedback data, comparing with the baselines.

Efficient application programming interface (API) recommendation is one of the most desired features of modern integrated development environments. A multitude of API recommendation approaches have been proposed. However, most of the currently available API recommenders do not support the effective integration of user feedback into the recommendation loop. In this paper, we present BRAID (Boosting RecommendAtion with Implicit FeeDback), a tool which leverages user feedback, and employs learning-to-rank and active learning techniques to boost recommendation performance. The implementation is based on the VSCode plugin architecture, which provides an integrated user interface. Essentially, BRAID is a general framework which can accommodate existing query-based API recommendation approaches as components. Comparative experiments with strong baselines demonstrate the efficacy of the tool. A video demonstrating the usage of BRAID can be found at https://youtu.be/naD0guvl8sE.

The emergence of Blockchain has attracted widespread attention. However, we observe that in practice, many applications on permissioned blockchains do not benefit from the decentralized architecture. When decentralized architecture is used but not required, system performance is often restricted, resulting in low throughput, high latency, and significant storage overhead. Hence, we propose LedgerDB on Alibaba Cloud, which is a centralized ledger database with tamper-evidence and non-repudiation features similar to blockchain, and provides strong auditability. LedgerDB has much higher throughput compared to blockchains. It offers stronger auditability by adopting a TSA two-way peg protocol, which prevents malicious behaviors from both users and service providers. LedgerDB supports verifiable data removals demanded by many real-world applications, which are able to remove obsolete records for storage saving and hide records for regulatory purpose, without compromising its verifiability. Experimental evaluation shows that LedgerDB's throughput is 80X higher than state-of-the-art permissioned blockchain (i.e., Hyperledger Fabric). Many blockchain customers (e.g., IP protection and supply chain) on Alibaba Cloud have switched to LedgerDB for its high throughput, low latency, strong auditability, and ease of use.