Stealth addresses represent an approach to enhancing privacy within public and distributed blockchains, such as Ethereum and Bitcoin. Stealth address protocols employ a distinct, randomly generated address for the recipient, thereby concealing interactions between entities. In this study, we introduce BaseSAP, an autonomous base-layer protocol for embedding stealth addresses within the application layer of programmable blockchains. BaseSAP expands upon previous research to develop a modular protocol for executing unlinkable transactions on public blockchains. BaseSAP allows for the development of additional stealth address layers using different cryptographic algorithms on top of the primary implementation, capitalizing on its modularity. To demonstrate the effectiveness of our proposed protocol, we present simulations of an advanced Secp256k1-based dual-key stealth address protocol. This protocol is developed on top of BaseSAP and deployed on the Ethereum test network as the first prototype implementation. Furthermore, we provide cost analyses and underscore potential security ramifications and attack vectors that could affect the privacy of stealth addresses. Our study highlights the flexibility of the BaseSAP protocol and provides insights into the broader implications of stealth address technology in the realm of blockchain privacy.

Recent progress in information extraction technology has enabled a vast array of applications that rely on structured data that is embedded in natural-language text. In particular, the extraction of concepts from the Web---with their desired attributes---is important to provide applications with rich, structured access to information. In this paper, we focus on an important family of concepts, namely, entities (e.g., people or organizations) and their attributes, and study how to efficiently and effectively extract them from Web-accessible text documents. Unfortunately, information extraction over the Web is challenging for both quality and efficiency reasons. Regarding quality, many sources on the Web contain misleading or invalid information; furthermore, extraction systems often return incorrect data. Regarding efficiency, information extraction is a time-consuming process, often involving expensive text-processing steps. We present a top-k extraction processing approach that addresses both the quality and efficiency challenges: for each entity and attribute of interest, we return the top-k values of the attribute for the entity according to a scoring function for extracted attribute values. This scoring function weighs the extraction confidence from individual documents, as well as the "importance" of the documents where the information originates. We define the document importance in terms of entity-specific document "popularity" statistics from a major search engine. Overall, our top-k extraction processing approach manages to identify the top attribute values for the entities of interest efficiently, as we demonstrate with a large-scale experimental evaluation over real-life data.