Broadcast-enhanced key predistribution schemes

We present a formalisation of a category of schemes that we refer to as broadcast-enhanced key predistribution schemes (BEKPSs). These schemes are suitable for networks with access to a trusted base station and an authenticated broadcast channel. We demonstrate that the access to these extra resources allows for the creation of BEKPSs with advantages over key predistribution schemes such as flexibility and more efficient revocation. There are many possible ways to implement BEKPSs, and we propose a framework for describing and analysing them. In their paper “From Key Predistribution to Key Redistribution,” Cichoń et al. [2010] propose a scheme for “redistributing” keys to a wireless sensor network using a broadcast channel after an initial key predistribution. We classify this as a BEKPS and analyse it in that context. We provide simpler proofs of some results from their paper, give a precise analysis of the resilience of their scheme, and discuss possible modifications. We then study two scenarios where BEKPSs may be particularly desirable and propose a suitable family of BEKPSs for each case. We demonstrate that they are practical and efficient to implement, and our analysis shows their effectiveness in achieving suitable trade-offs between the conflicting priorities in resource-constrained networks

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

PIKE: Peer intermediaries for key establishment in sensor networks

Abstract — The establishment of shared cryptographic keys between communicating neighbor nodes in sensor networks is a challenging problem due to the unsuitability of asymmetric key cryptography for these resource-constrained platforms. A range of symmetric-key distribution protocols exist, but these protocols do not scale effectively to large sensor networks. For a given level of security, each protocol incurs a linearly increasing overhead in either communication cost per node or memory per node. We describe Peer Intermediaries for Key Establishment (PIKE), a class of key-establishment protocols that involves using one or more sensor nodes as a trusted intermediary to facilitate key establishment. We show that, unlike existing key-establishment protocols, both the communication and memory overheads of PIKE protocols scale sub-linearly (O ( √ n)) with the number of nodes in the network yet achieving higher security against node compromise than other protocols. I

ABSTRACT Secure Hierarchical In-Network Aggregation in Sensor Networks ∗

In-network aggregation is an essential primitive for performing queries on sensor network data. However, most aggregation algorithms assume that all intermediate nodes are trusted. In contrast, the standard threat model in sensor network security assumes that an attacker may control a fraction of the nodes, which may misbehave in an arbitrary (Byzantine) manner. We present the first algorithm for provably secure hierarchical in-network data aggregation. Our algorithm is guaranteed to detect any manipulation of the aggregate by the adversary beyond what is achievable through direct injection of data values at compromised nodes. In other words, the adversary can never gain any advantage from misrepresenting intermediate aggregation computations. Our algorithm incurs only O(∆log 2 n) node congestion, supports arbitrary tree-based aggregator topologies and retains its resistance against aggregation manipulation in the presence of arbitrary numbers of malicious nodes. The main algorithm is based on performing the SUM aggregation securely by first forcing the adversary to commit to its choice of intermediate aggregation results, and then having the sensor nodes independently verify that their contributions to the aggregate are correctly incorporated. We show how to reduce secure MEDIAN, COUNT, and AVERAGE to this primitive

15-854: Approximations Algorithms Lecturer: Anupam Gupta Topic: Max-cut, Hardness of Approximations Date: 9/14

Given an undirected graph G = (V,E), a cut in G is a subset S ⊆ V. Let S = V \S, and let E(S,S) denote the set of edges with one vertex in S and one vertex in S. We will use the term “cut ” to refer variously to the partition (S,S) of the vertices, and also the set of edges E(S,S) crossing between S and S; typically, we will use this term in a way that is unambiguous, and will disambiguate where necessary. The Max-Cut problem is to find the cut S that maximizes |E(S,S)|. In the weighted version of Max-Cut, we are also given a edge weight function w: E → R, and the problem is to find a cut with maximum weight. The Max-Cut problem is NP-hard, and is interesting to contrast with the Min-Cut problem, which is solvable in polynomial time (e.g., by reducing to the s-t min-cut problem, which is dual to the Max-Flow problem, and is solvable by algorithms such as one by Edmonds and Karp [1]. 2.1.1 Approx Max-Cut using Local Search THe general idea in local search algorithms is the same: Start with a solution, find an improving step to make a better solution, and repeat until stuck. In more detail: 1. Start with some arbitrary S0 ⊆ V