Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.In this paper we consider the problem of scalable and secure data publication from IoT devices, with included mechanisms that help towards device attacks prevention and detection. We propose SEDIMENT, a system and methodology which look more specifically at problems that arise in a network with a broad variety of devices, some of which have limited resources and some of which were designed for a less hostile environment. SEDIMENT uses a combination of software root of trust, remote attestation and resource-efficient cryptography, to build a system that scales across heterogeneous computing platforms. It allows for devices that range from battery-powered devices that are intended to operate for long periods up to server-class machines without power constraints. SEDIMENT provides a secure application layer that can be used for common communication paradigms such as publish-subscribe while following zero-trust principles in both protecting the end hosts from the network and other end hosts, as well as protecting the network from the end hosts.

We investigate the problem of delegating operations in cryptography solutions from client devices that only perform quasilinear-time or lower-order computations (e.g., additions/subtractions, modular reductions with a small modulus, etc.) to a single, possibly malicious, server. All previous work considered clients capable of computing higher-order operations, such as fully homomorphic encryption, group exponentiations or several group multiplications. In this model, we show protocols to delegate the computation of ring multiplications while satisfying desirable result correctness, input privacy and result security requirements. The main technical component, of independent interest, is a family of probabilistic tests that extends a classical test by Pippenger. The asymptotic improvement in our multiplication delegation protocols is also backed up by concrete implementation results, demonstrating that the client's online computation is strictly smaller than non-delegated computation of the same function, for input lengths of interest in cryptography solutions.

Abstract Confidential Content-Based Publish/Subscribe (C-CBPS) is an interaction model that allows parties to exchange content while protecting their security and privacy interests. In this paper we advance the state of the art in C-CBPS by showing how all predicate circuits in NC1 (logarithmic-depth, bounded fan-in) can be confidentially computed by a broker while guaranteeing perfect information-theoretic security. Previous work could handle only strictly shallower circuits (e.g. those with depth O(ℑ)). We present three protocols—UGP-Match, FSGP-Match and OFSGP-Match—based on 2-decomposable randomized encodings of group programs for circuits in NC1. UGP-Match is conceptually simple and has a clean proof of correctness but its running time is a polynomial with a high exponent and hence impractical. FSGP-Match uses a “fixed structure” construction that reduces the exponent drastically and achieves efficiency and scalability. OFSGP-Match optimizes the group programs further to shave off a linear factor.

Distributed self-organization algorithms for wireless sensor (and actuator) networks must have low message complexity from energy and bandwidth considerations. In this paper, we present a novel approach for message-efficient clustering, in which nodes allocate local growth budgets to neighbors. We introduce two algorithms that make use of this approach. Unlike the expanding ring approach [C.V. Ramamoorhty, A. Bhide, and J. Srivastava, Proc. IEEE INFOCOM '87, 1987], our algorithms do not involve the initiator in each round, and do not violate the specified upper bound on the cluster size at any time. We derive analytical performance bounds of our algorithms and also provide performance results from simulations. The algorithms produce clusters of bounded size and low diameter, using significantly fewer messages than the expanding ring approach.

Distributed denial of service (DDoS) attacks exploit the acute imbalance between client and server workloads to cause devastation to the service providers. We propose a distributed gateway architecture and a payment protocol that imposes dynamically changing prices on network, server, and information resources in order to push some cost of initiating service requests - in terms of monetary payments and/or computational burdens back onto the requesting clients. By employing different price and purchase functions, the architecture can provide service quality differentiation and furthermore, select good client behavior and discriminate against adversarial behavior. If confirmed by additional experiments, judicious partitioning of resources using different pricing functions can improve overall service survivability.

Emerging network technologies offer new design choices for performing traffic engineering and management, while introducing challenges for existing protocol control mechanisms. There is a tension between new hop-by-hop capabilities and existing end-to-end mechanisms. We examine one such capability, the load-reactive link (LRL), which has a capacity that can be automatically varied in response to the offered load. Before such adaptive links can be considered for deployment, it is essential to understand the behavior of widely deployed end-to-end protocols, in particular TCP. We study the behavior of TCP flows over a particular type of LRL that uses a hysteresis control mechanism for capacity allocation that reacts to current traffic loads. Our results indicate that reasonable performance for a single TCP flow can be achieved with careful choice of control parameters. We also show that poor choice of control parameters make it difficult or impossible for TCP to function. Preliminary simulations have been done with multiple TCP flows, but further investigation is needed to understand the behavior of the aggregation of a large number of flows.

As networks grow in scale and heterogeneity, hierarchical organization is inevitable. We present the case for optimal self-organization of network hierarchies. We provide a graph partitioning formulation relevant to networking but different from extant graph partitioning literature. In particular, we require that the resultant partitions be connected, a constraint that changes the character of the problem significantly. We devise a solution that consists of distinct phases for initial partitioning, refinement and post-processing, propose efficient heuristics for each phase, and evaluate them extensively on internetwork-like graphs through simulation. The results suggest that vertex trading techniques, in vogue for a number of decades in graph partitioning, are highly applicable, but multilevel techniques favored by conventional graph partitioning research may be of limited value for internetwork-like graphs. This solution can be implemented in practical networks to automatically generate Internet OSPF areas or ATM PNNI clusters.

Large scale clustered continuous media (CM) servers deployed in applications like video-on-demand have high availability requirements.In the event of server failure, streams from the failed servers must be reassigned to healthy servers with minimum service disruption.Such servers may also suffer from periods of transient overload resulting from a high degree of customer interactivity.For example, in a video-on-demand system if a large number of users are viewing a favorite game, many of them can simultaneously request a replay of an interesting part of the game.This requires a large number of '5nteractive" channels within a short period of time and can result in a transient server overload.In this paper we propose solutions for graceful recovery from overload scenarios arising out of server failure or customer interactions.Rapid resource reclamation is key to overload tolerance, and our proposed solution is based on rate adaptive stream merging and content insertion techniques, We also utilize conventional time-sharing techniques to handle transient overload.We show that while merging is necessary for achieving overload tolerance, it is not sufficient, and for a complete solution, content insertion is required.Specifically, we consider a general clustered CM server architecture model where multiple servers can fail simultaneously.We develop a model for resource shortfalls that occur as a result of overload on failure.We also describe optimal polynomial time algorithms for recovering resources to the maximum extent possible, by clustering streams in real time.