We read with great appreciation the article by Park and colleagues describing the incorporation of DICOM terminology into the OMOP Common Data Model (MI-CDM) to support multimodal observational research.1 The study effectively addresses a long-standing challenge—bridging rich imaging metadata with standardized electronic health record (EHR) data—and we commend the authors for the rigor and transparency of their approach. The integration of 5183 DICOM Attributes, 3628 coded values, and 16 958 OMOP concepts, alongside the ingestion of 545 Alzheimer’s Disease Neuroimaging Initiative (ADNI) imaging studies comprising 4756 series and 691 224 harvested metadata values, demonstrates impressive technical achievement and reproducibility. The decision to share the processing pipeline publicly is especially commendable, as it invites further community validation and use.1 While the work convincingly demonstrates feasibility, a few clarifications could enhance its practical utility for large-scale implementation. The manuscript reports that 691 224 metadata values were harvested, yet 283 948 elements were ultimately loaded into the Image_feature and Measurement tables after filtering.1 A brief clarification of the filtering process that led to this reduction—such as the number of empty attributes removed, the extent of deduplication, or the exclusion criteria for certain Value Representation (VR) types—would make the workflow easier to reproduce. Providing a concise summary of how the raw, filtered, and loaded counts evolved, perhaps grouped by VR category (eg, CS, DS, FD, LO), would allow future users to benchmark their own ETL pipelines against the authors’ results.

The growing demand for computing power drives more artificial intelligence (AI) model owners to outsource their models to public clouds, relying on cloud servers to manage which users can use, train, or upgrade AI models. Unfortunately, existing work cannot simultaneously manage the entire model lifecycle in public clouds when considering model stealing attacks, where cloud servers covertly replicate AI models and deliver AI services to unauthorized users for profit. As a result, model owners are forced to conduct training and upgrades in private environments before deploying models to clouds for service delivery, which poses significant challenges in collaboration and maintenance, particularly for models requiring frequent upgrades. In this paper, we introduce$\mathsf {Trident}$, the first secure cloud-based framework for flexible AI model lifecycle management, including availability, trainability, and upgradability. By leveraging multiple cryptographic techniques, such as access control trees,$\mathsf {Trident}$ensures that AI models and their management policies are tightly coupled, compelling cloud servers to execute only specified model operations without violating management policies, thereby resisting model stealing attacks. Rather than straightforward cryptographic applications, we address a series of technical challenges, including shifting the focus of access control trees from data to model management and maintaining downward-compatible model management rights. We propose two detailed constructions: Semi-$\mathsf {Trident}$and Full-$\mathsf {Trident}$, tailored for semi-delegation and full-delegation scenarios, i.e., whether model owners need to interact with cloud servers while delivering AI services. Theoretical complexity analysis and security analysis prove the competitive efficiency and security. Experimental results show that compared to assembling existing partial-function schemes, Semi-$\mathsf {Trident}$and Full-$\mathsf {Trident}$achieve around$3.6\times$improvement in time costs and$5\times$improvement in communication overhead.

Nowadays, checkpoints have been proven to be an effective solution to ensure slashability in proof-of-stake (PoS) consensus, and Tas et al.'s cutting-edge solution in S&P 2023 is a typical example. Unfortunately, despite progress, hour-level transaction delays and annually around 10 K dollar checkpoint costs make existing related solutions still unacceptable in realworld PoS applications. In this paper, we propose SecPoS, a slashable PoS consensus with second-level transaction delays and one-time checkpoint costs. To achieve these design goals, we draw inspiration from Pixel+ signatures and chameleon hash functions to design a novel bilateral blockchain structure, achieving twoblock transaction finalization via only uploading the first block of our chain as checkpoints. Next, considering practical application requirements, we address a series of following challenges, such as bilateral immutability, blockchain forks, determination of the main chain, and malicious attacks from PoS members. In detail, we propose two constructions of SecPoS, i.e., SecPoS – A and SecPoS – B. SecPoS – A and SecPoS – B have a tradeoff between transaction delays and block numbers packed in an epoch. Compatible with most existing one-way blockchains, we implement and outsource a prototype SecPoS to facilitate research11https://github.com/Academic-Paper-Codes/SecPoS-Consensus, and prove the security of SecPoS. Experiments on this prototype show that SecPoS – A and SecPoS – B require around 5s and 100s transaction delays, respectively, and both require 2 dollars one-time checkpoint costs.