GraphSE2^2: An Encrypted Graph Database for Privacy-Preserving Social Search

In this paper, we propose GraphSE$^2$, an encrypted graph database for online social network services to address massive data breaches. GraphSE$^2$ preserves the functionality of social search, a key enabler for quality social network services, where social search queries are conducted on a large-scale social graph and meanwhile perform set and computational operations on user-generated contents. To enable efficient privacy-preserving social search, GraphSE$^2$ provides an encrypted structural data model to facilitate parallel and encrypted graph data access. It is also designed to decompose complex social search queries into atomic operations and realise them via interchangeable protocols in a fast and scalable manner. We build GraphSE$^2$ with various queries supported in the Facebook graph search engine and implement a full-fledged prototype. Extensive evaluations on Azure Cloud demonstrate that GraphSE$^2$ is practical for querying a social graph with a million of users.Comment: This is the full version of our AsiaCCS paper "GraphSE$^2$: An Encrypted Graph Database for Privacy-Preserving Social Search". It includes the security proof of the proposed scheme. If you want to cite our work, please cite the conference version of i

Improved Approximation Factor for Adaptive Influence Maximization via Simple Greedy Strategies

In the adaptive influence maximization problem, we are given a social network and a budget k, and we iteratively select k nodes, called seeds, in order to maximize the expected number of nodes that are reached by an influence cascade that they generate according to a stochastic model for influence diffusion. The decision on the next seed to select is based on the observed cascade of previously selected seeds. We focus on the myopic feedback model, in which we can only observe which neighbors of previously selected seeds have been influenced and on the independent cascade model, where each edge is associated with an independent probability of diffusing influence. While adaptive policies are strictly stronger than non-adaptive ones, in which all the seeds are selected beforehand, the latter are much easier to design and implement and they provide good approximation factors if the adaptivity gap, the ratio between the adaptive and the non-adaptive optima, is small. Previous works showed that the adaptivity gap is at most 4, and that simple adaptive or non-adaptive greedy algorithms guarantee an approximation of 1/4 (1-1/e) ? 0.158 for the adaptive optimum. This is the best approximation factor known so far for the adaptive influence maximization problem with myopic feedback. In this paper, we directly analyze the approximation factor of the non-adaptive greedy algorithm, without passing through the adaptivity gap, and show an improved bound of 1/2 (1-1/e) ? 0.316. Therefore, the adaptivity gap is at most 2e/e-1 ? 3.164. To prove these bounds, we introduce a new approach to relate the greedy non-adaptive algorithm to the adaptive optimum. The new approach does not rely on multi-linear extensions or random walks on optimal decision trees, which are commonly used techniques in the field. We believe that it is of independent interest and may be used to analyze other adaptive optimization problems. Finally, we also analyze the adaptive greedy algorithm, and show that guarantees an improved approximation factor of 1-1/(?{e)}? 0.393

Do glucosamine and chondroitin worsen blood sugar control in diabetes?

Despite theoretical risks based on animal models given high intravenous doses, glucosamine/chondroitin (1500 mg/1200 mg daily) does not adversely affect short-term glycemic control for patients whose diabetes is well-controlled, or for those without diabetes or glucose intolerance (SOR: A, consistent, good-quality patient-oriented evidence). Some preliminary evidence suggests that glucosamine may worsen glucose intolerance for patients with untreated or undiagnosed glucose intolerance or diabetes (SOR: C, extrapolation from disease-oriented evidence)

Scaling of NonOhmic Conduction in Strongly Correlated Systems

A new scaling formalism is used to analyze nonlinear I-V data in the vicinity of metal-insulator transitions (MIT) in five manganite systems. An exponent, called the nonlinearity exponent, and an onset field for nonlinearity, both characteristic of the system under study, are obtained from the analysis. The onset field is found to have an anomalously low value corroborating the theoretically predicted electronically soft phases. The scaling functions above and below the MIT of a polycrystalline sample are found to be the same but with different exponents which are attributed to the distribution of the MIT temperatures. The applicability of the scaling in manganites underlines the universal response of the disordered systems to electric field