In this paper, we study a class of set cover problems that satisfy a special property which we call the small neighborhood cover property. This class encompasses several well-studied problems including vertex cover, interval cover, bag interval cover and tree cover. We design unified distributed and parallel algorithms that can handle any set cover problem falling under the above framework and yield constant factor approximations. These algorithms run in polylogarithmic communication rounds in the distributed setting and are in NC, in the parallel setting.

The problem of privatizing statistical databases is a well-studied topic that has culminated with the notion of differential privacy. The complementary problem of securing these differentially private databases, however, has—as far as we know—not been considered in the past. While the security of private databases is in theory orthogonal to the problem of private statistical analysis (e.g., in the central model of differential privacy the curator is trusted) the recent real-world deployments of differentially-private systems suggest that it will become a problem of increasing importance. In this work, we consider the problem of designing encrypted databases (EDB) that support differentially-private statistical queries. More precisely, these EDBs should support a set of encrypted operations with which a curator can securely query and manage its data, and a set of private operations with which an analyst can privately analyze the data. Using such an EDB, a curator can securely outsource its database to an untrusted server (e.g., on-premise or in the cloud) while still allowing an analyst to privately query it. We show how to design an EDB that supports private histogram queries. As a building block, we introduce a differentially-private encrypted counter based on the binary mechanism of Chan et al. (ICALP, 2010). We then carefully combine multiple instances of this counter with a standard encrypted database scheme to support differentially-private histogram queries.

Sixteen eyes with absolute glaucoma were treated and followed up for a minimum of six months. Goniotomy ab interno using the Fugo Blade was found to be a safe alternative to conventional trabeculectomy, which safely and effectively reduced intraocular pressure in more than 80% of cases.

Blockchain databases are storage systems that combine properties of blockchains and databases like decentralization, tamperproofness, low query latency and support for complex queries. Blockchain databases are an emerging and important class of blockchain technology that is critical to the development of non-trivial smart contracts, distributed applications and decentralized marketplaces. In this work, we consider the problem of designing end-to-end encrypted blockchain databases to support the development of decentralized applications that need to store and query sensitive data. In particular, we show how to design what we call blockchain encrypted multi-maps (EMM) which can be used to instantiate various kinds of NoSQL blockchain databases like key-value stores or document databases. We propose three blockchain EMM constructions, each of which achieves different tradeoffs between query, add and delete efficiency. All of our constructions are legacy-friendly in the sense that they can be implemented on top of any existing blockchain. This is particularly challenging since blockchains do not support data deletion. We implemented our schemes on the Algorand blockchain and evaluated their concrete efficiency empirically. Our experiments show that they are practical. CCS CONCEPTS • Security and privacy → Management and querying of encrypted data; Cryptography;