arXiv subjects

Blake Regalia

Publications and source records attributed to Blake Regalia.

4 recordsLinked to original sources

arXiv2026

Data Structures for Private Token Transfers in TEE-Based Networks

Trusted execution environment (TEE) based confidential smart contract networks promise privacy but remain vulnerable to storage access pattern attacks that can link senders and recipients in token transfers. When contracts update recipient balances during transfers, the unique storage keys accessed reveal transaction relationships even when data is encrypted. This paper introduces two novel data structures to address this vulnerability: the Delayed Write Buffer (DWB) and the Bitwise-Trie of Bucketed Entries (BTBE). The DWB delays recipient balance updates by buffering pending transfers and randomly settling entries, breaking the direct correlation between transfer execution and recipient storage access. The BTBE further enhances privacy by grouping addresses into constant-sized buckets, preventing flooding attacks and creating anonymity sets for balance queries. Additionally, we present a private notification system enabling real-time, privacy-preserving push notifications for confidential contracts. Our domain-specific approach leverages the unique characteristics of token transfers -- asymmetric balance updates and tolerance for delayed settlement -- to achieve practical performance with probabilistic anonymity guarantees.

cs.CR↗

arXiv2024

Non-Fungible Programs: Private Full-Stack Applications for Web3

The greatest advantage that Web3 applications offer over Web 2.0 is the evolution of the data access layer. Opaque, centralized services that compelled trust from users are replaced by trustless, decentralized systems of smart contracts. However, the public nature of blockchain-based databases, on which smart contracts transact, has typically presented a challenge for applications that depend on data privacy or that rely on participants having incomplete information. This has changed with the introduction of confidential smart contract networks that encrypt the memory state of active contracts as well as their databases stored on-chain. With confidentiality, contracts can more readily implement novel interaction mechanisms that were previously infeasible. Meanwhile, in both Web 2.0 and Web3 applications the user interface continues to play a crucial role in translating user intent into actionable requests. In many cases, developers have shifted intelligence and autonomy into the client-side, leveraging Web technologies for compute, graphics, and networking. Web3's reliance on such frontends has revealed a pain point though, namely that decentralized applications are not accessible to end users without a persistent host serving the application. Here we introduce the Non-Fungible Program (NFP) model for developing self-contained frontend applications that are distributed via blockchain, powered by Web technology, and backed by private databases persisted in encrypted smart contracts. Access to frontend code, as well as backend services, is controlled and guaranteed by smart contracts according to the NFT ownership model, eliminating the need for a separate host. By extension, NFP applications bring interactivity to token owners and enable new functionalities, such as authorization mechanisms for oracles, supplementary Web services, and overlay networks in a secure manner. In addition...

cs.CY↗

arXiv2020

SE-KGE: A Location-Aware Knowledge Graph Embedding Model for Geographic Question Answering and Spatial Semantic Lifting

Learning knowledge graph (KG) embeddings is an emerging technique for a variety of downstream tasks such as summarization, link prediction, information retrieval, and question answering. However, most existing KG embedding models neglect space and, therefore, do not perform well when applied to (geo)spatial data and tasks. For those models that consider space, most of them primarily rely on some notions of distance. These models suffer from higher computational complexity during training while still losing information beyond the relative distance between entities. In this work, we propose a location-aware KG embedding model called SE-KGE. It directly encodes spatial information such as point coordinates or bounding boxes of geographic entities into the KG embedding space. The resulting model is capable of handling different types of spatial reasoning. We also construct a geographic knowledge graph as well as a set of geographic query-answer pairs called DBGeo to evaluate the performance of SE-KGE in comparison to multiple baselines. Evaluation results show that SE-KGE outperforms these baselines on the DBGeo dataset for geographic logic query answering task. This demonstrates the effectiveness of our spatially-explicit model and the importance of considering the scale of different geographic entities. Finally, we introduce a novel downstream task called spatial semantic lifting which links an arbitrary location in the study area to entities in the KG via some relations. Evaluation on DBGeo shows that our model outperforms the baseline by a substantial margin.

cs.DB↗

arXiv2020

Semantically-Enriched Search Engine for Geoportals: A Case Study with ArcGIS Online

Many geoportals such as ArcGIS Online are established with the goal of improving geospatial data reusability and achieving intelligent knowledge discovery. However, according to previous research, most of the existing geoportals adopt Lucene-based techniques to achieve their core search functionality, which has a limited ability to capture the user's search intentions. To better understand a user's search intention, query expansion can be used to enrich the user's query by adding semantically similar terms. In the context of geoportals and geographic information retrieval, we advocate the idea of semantically enriching a user's query from both geospatial and thematic perspectives. In the geospatial aspect, we propose to enrich a query by using both place partonomy and distance decay. In terms of the thematic aspect, concept expansion and embedding-based document similarity are used to infer the implicit information hidden in a user's query. This semantic query expansion 1 2 G. Mai et al. framework is implemented as a semantically-enriched search engine using ArcGIS Online as a case study. A benchmark dataset is constructed to evaluate the proposed framework. Our evaluation results show that the proposed semantic query expansion framework is very effective in capturing a user's search intention and significantly outperforms a well-established baseline-Lucene's practical scoring function-with more than 3.0 increments in DCG@K (K=3,5,10).

cs.IR↗