A reachability oracle (or hop labeling) assigns each vertex v two sets of vertices: Lout(v) and Lin(v), such that u reaches v iff Lout(u) ∩ Lin(v) = ∅. Despite their simplicity and elegance, reachability oracles have failed to achieve efficiency in more than ten years since their introduction: The main problem is high construction cost, which stems from a set-cover framework and the need to materialize transitive closure. In this paper, we present two simple and efficient labeling algorithms, Hierarchical-Labeling and Distribution-Labeling, which can work on massive real-world graphs: Their construction time is an order of magnitude faster than the set-cover based labeling approach, and transitive closure materialization is not needed. On large graphs, their index sizes and their query performance can now beat the state-of-the-art transitive closure compression and online search approaches.

According to a wide range of studies, IT should become a key facilitator in establishing primary education, reducing mortality and supporting commercial initiatives in Least Developed Countries (LDCs). The main barrier to the development of IT services in these regions is not only the lack of communication facilities, but also the lack of consistent information systems, security procedures, economic and legal support, as well as political commitment. In this paper, we propose the vision of an infrastructureless data platform well suited for the development of innovative IT services in LDCs. We propose a participatory approach, where each individual implements a small subset of a complete information system thanks to highly secure, portable and low-cost personal devices as well as opportunistic networking, without the need of any form of infrastructure. We review the technical challenges that are specific to this approach. 1.

Social relationships between people, e.g., whether they are friends with each other, can be inferred by observing their behaviors in the real world. Due to the popularity of GPS-enabled mobile devices or online services, a large amount of high-resolution spatiotemporal location data becomes avail-able for such inference studies. However, due to the sensitiv-ity of location data and user privacy concerns, those studies cannot be largely carried out on individually contributed data without privacy guarantees. Furthermore, we observe that the actual location may not be needed for social rela-tionship studies, but rather the fact that two people meet and some statistical properties about their meeting location, which can be computed in a private manner. In this paper, we envision a novel extensible framework, dubbed Privacy-preserving Location Analytics and Computation Environ-ment (PLACE), which enables social relationship studies by analyzing individually generated location data. PLACE uti-lizes an untrusted server and computes the building blocks to support various social relationship studies, without disclos-ing location information to the server and other untrusted parties. We showcase PLACE with three use cases and four novel building blocks and ensure privacy for block compu-tation with encryption and differential privacy primitives. The successful realization of PLACE will facilitate private location data acquisition from individual devices, thanks to the strong privacy guarantees, and will enable a wide range of applications. 1.