Distributed ledgers provide high availability and integrity , making them a key enabler for practical and secure computation of distributed workloads among mutually distrustful parties. Many practical applications also require strong confidentiality , however. This work enhances permissioned and permissionless blockchains with the ability to manage confidential data without forfeiting availability or decentralization. The proposed Calypso architecture addresses two orthogonal challenges confronting modern distributed ledgers: (a) enabling the auditable management of secrets and (b) protecting distributed computations against arbitrage attacks when their results depend on the ordering and secrecy of inputs. Calypso introduces on-chain secrets, a novel abstraction that enforces atomic deposition of an auditable trace whenever users access confidential data. Calypso provides user-controlled consent management that ensures revocation atomicity and accountable anonymity. To enable permissionless deployment, we introduce an incentive scheme and provide users with the option to select their preferred trustees. We evaluated our Calypso prototype with a confidential document-sharing application and a decentralized lottery. Our benchmarks show that transaction-processing latency increases linearly in terms of security (number of trustees) and is in the range of 0.2 to 8 seconds for 16 to 128 trustees.

While showing great promise, smart contracts are difficult to program correctly, as they need a deep understanding of cryptography and distributed algorithms, and offer limited functionality, as they have to be deterministic and cannot operate on secret data. In this paper we present Protean, a general-purpose decentralized computing platform that addresses these limitations by moving from a monolithic execution model, where all participating nodes store all the state and execute every computation, to a modular execution-model. Protean employs secure specialized modules, called functional units, for building decentralized applications that are currently insecure or impossible to implement with smart contracts. Each functional unit is a distributed system that provides a special-purpose functionality by exposing atomic transactions to the smart-contract developer. Combining these transactions into arbitrarily-defined workflows, developers can build a larger class of decentralized applications, such as provably-secure and fair lotteries or e-voting.

Datasets that organize and associate the many identifiers produced by PCs, smartphones, and tablets accessing the internet are referred to as internet device graphs . In this paper, we demonstrate how measurement, tracking, and other internet entities can associate multiple identifiers with a single device or user after coarse associations, e.g ., based on IP-colocation , are made. We employ a Bayesian similarity algorithm that relies on examples of pairs of identifiers and their associated telemetry, including user agent, screen size, and domains visited, to establish pair-wise scores. Community detection algorithms are applied to group identifiers that belong to the same device or user. We train and validate our methodology using a unique dataset collected from a client panel with full visibility, apply it to a dataset of 700 million device identifiers collected over the course of six weeks in the United States, and show that it outperforms several unsupervised learning approaches. Results show mean precision and recall exceeding 90% for association of identifiers at both the device and user levels.

Internet device graphs identify relationships between user-centric internet connected devices such as desktops, laptops, smartphones, tablets, gaming consoles, TV's, etc. The ability to create such graphs is compelling for online advertising, content customization, recommendation systems, security, and operations. We begin by describing an algorithm for generating a device graph based on IP-colocation, and then apply the algorithm to a corpus of over 2.5 trillion internet events collected over the period of six weeks in the United States. The resulting graph exhibits immense scale with greater than 7.3 billion edges (pair-wise relationships) between more than 1.2 billion nodes (devices), accounting for the vast majority of internet connected devices in the US. Next, we apply community detection algorithms to the graph resulting in a partitioning of internet devices into 100 million small communities representing physical households. We validate this partition with a unique ground truth dataset. We report on the characteristics of the graph and the communities. Lastly, we discuss the important issues of ethics and privacy that must be considered when creating and studying device graphs, and suggest further opportunities for device graph enrichment and application.