6130133
Security and Privacy in Sensor Networks
Sensor networks offer economically viable solutions for a variety of applications. For example, current implementations monitor factory instrumentation, pollution levels, freeway traffic, and the structural integrity of buildings. Other applications include climate sensing and control in office buildings and home environmental sensing systems for temperature, light, moisture, and motion. Sensor networks are key to the creation of smart spaces, which embed information technology in everyday home and work environments. The miniature wireless sensor nodes, or motes, developed from low-cost off-the-shelf components at the University of California, Berkeley, as part of its smart dust projects, establish a self-organizing sensor network when dispersed into an environment. The privacy and security issues posed by sensor networks represent a rich field of research problems. Improving network hardware and software may address many of the issues, but others will require new supporting technologies.
12254807
PIKE: peer intermediaries for key establishment in sensor networks
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(/spl radic/n)) with the number of nodes in the network yet achieving higher security against node compromise than other protocols.
5074364
Modeling adoptability of secure BGP protocols
Despite the existence of several secure BGP routing protocols, there has been little progress to date on actual adoption. Although feasibility for widespread adoption remains the greatest hurdle for BGP security, there has been little quantitative research into what properties contribute the most to the adoptability of a security scheme. In this paper, we provide a model for assessing the adoptability of a secure BGP routing protocol. We perform this evaluation by simulating incentives compatible adoption decisions of ISPs on the Internet under a variety of assumptions. Our results include: (a) the existence of a sharp threshold, where, if the cost of adoption is below the threshold, complete adoption takes place, while almost no adoption takes place above the threshold; (b) under a strong attacker model, adding a single hop of path authentication to origin authentication yields similar adoptability characteristics as a full path security scheme; (c) under a weaker attacker model, adding full path authentication (e.g., via S-BGP [9]) significantly improves the adoptability of BGP security over weaker path security schemes such as soBGP [16]. These results provide insight into the development of more adoptable secure BGP protocols and demonstrate the importance of studying adoptability of protocols.
239370
SIA: secure information aggregation in sensor networks
Sensor networks promise viable solutions to many monitoring problems. However, the practical deployment of sensor networks faces many challenges imposed by real-world demands. Sensor nodes often have limited computation and communication resources and battery power. Moreover, in many applications sensors are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the sensor's cryptographic keys.One of the basic and indispensable functionalities of sensor networks is the ability to answer queries over the data acquired by the sensors. The resource constraints and security issues make designing mechanisms for information aggregation in large sensor networks particularly challenging.In this paper, we propose a novel framework for secure information aggregation in large sensor networks. In our framework certain nodes in the sensor network, called aggregators, help aggregating information requested by a query, which substantially reduces the communication overhead. By constructing efficient random sampling mechanisms and interactive proofs, we enable the user to verify that the answer given by the aggregator is a good approximation of the true value even when the aggregator and a fraction of the sensor nodes are corrupted. In particular, we present efficient protocols for secure computation of the median and the average of the measurements, for the estimation of the network size, and for finding the minimum and maximum sensor reading. Our protocols require only sublinear communication between the aggregator and the user. To the best of our knowledge, this paper is the first on secure information aggregation in sensor networks that can handle a malicious aggregator and sensor nodes.
4540744
Random key predistribution schemes for sensor networks
Key establishment in sensor networks is a challenging problem because asymmetric key cryptosystems are unsuitable for use in resource constrained sensor nodes, and also because the nodes could be physically compromised by an adversary. We present three new mechanisms for key establishment using the framework of pre-distributing a random set of keys to each node. First, in the q-composite keys scheme, we trade off the unlikeliness of a large-scale network attack in order to significantly strengthen random key predistribution's strength against smaller-scale attacks. Second, in the multipath-reinforcement scheme, we show how to strengthen the security between any two nodes by leveraging the security of other links. Finally, we present the random-pairwise keys scheme, which perfectly preserves the secrecy of the rest of the network when any node is captured, and also enables node-to-node authentication and quorum-based revocation.
182657
Key infection: smart trust for smart dust
Future distributed systems may include large self-organizing networks of locally communicating sensor nodes, any small number of which may be subvened by an adversary. Providing security for these sensor networks is important, but the problem is complicated by the fact that managing cryptographic key material is hard: low-cost nodes are neither tamper-proof nor capable of performing public key cryptography efficiently. We show how the key distribution problem can be dealt with in environments with a partially present, passive adversary: a node wishing to communicate securely with other nodes simply generates a symmetric key and sends it in the clear to its neighbours. Despite the apparent insecurity of this primitive, we can use mechanisms for key updating, multipath secrecy amplification and multihop key propagation to build up extremely resilient trust networks where at most a fixed proportion of communications links can be eavesdropped. We discuss applications in which this assumption is sensible. Many systems must perforce cope with principals who are authenticated weakly, if at all; the resulting issues have often been left in the 'too hard' tray. One particular interest of sensor networks is that they present a sufficiently compact and tractable version of this problem. We can perform quantitative analyses and simulations of alternative strategies, some of which we present here. We also hope that This work may start to challenge the common belief that authentication is substantially about bootstrapping trust. We argue that, in distributed systems where the opponent can subvert any small proportion of nodes, it is more economic to invest in resilience than in bootstrapping.
15483673
Process Isolation for Reconfigurable Hardware
One of the pillars of trust-worthy computing is process isolation, the ability to keep process data private from other processes running on the same device. While embedded operating systems provide isolation for the software part of these processes, there is no commonly accepted isolation mechanism for the hardware resources. As a result, systems may remain vulnerable to hardware-based attacks. This paper presents a secure coprocessor interface that extends the concept of process isolation into reconfigurable hardware. In the resulting coprocessor design, context information for different processes concurrently accessing the coprocessor is physically kept private. The coprocessor interface can handle context switches between different processes without assistance of the operating system. Because of this, reconfiguration of computation units can occur independent of the main processor. Moreover, it does so with greater efficiency than what is possible using software only.
5935513
On Bayesian Network Approximation by Edge Deletion
We consider the problem of deleting edges from a Bayesian network for the purpose of simplifying models in probabilistic inference. In particular, we propose a new method for deleting network edges, which is based on the evidence at hand. We provide some interesting bounds on the KL-divergence between original and approximate networks, which highlight the impact of given evidence on the quality of approximation and shed some light on good and bad candidates for edge deletion. We finally demonstrate empirically the promise of the proposed edge deletion technique as a basis for approximate inference.
9263402
Artificial convolution neural network for medical image pattern recognition
Abstract We have developed several training methods in conjunction with a convolution neural network for general medical image pattern recognition. An unconventional method of using rotation and shift invariance is also proposed to enhance the neural net performance. The structure of the artificial neural network is a simplified network structure of the neocognitron. Two-dimensional local connection as a group is the fundamental architecture for the signal propagation in the convolution neural network. Weighting coefficients of convolution kernels are formed by the neural network through backpropagated training for this artificial neural net. In addition, radiologists' reading procedure was modelled in order to instruct the artificial neural network to recognize the predefined image patterns and those of interest to experts. Our training techniques involve (a) radiologists' rating for each suspected image area, (b) backpropagation of generalized distribution, (c) trainer imposed functions, (d) shift and rotation invariance of diagnosis interpretation, and (e) consistency of clinical input data using appropriate background reduction functions. We have tested these methods for detecting lung nodules on chest radiographs and microcalcications on mammograms. The performance studies have shown the potential use of this technique in a clinical environment. We also used a profile double-matching technique for initial nodule search and used a wavelet high pass filtering technique to enhance subtle clustered microcalcifications. We set searching parameters at a highly sensitive level to identify all potential disease areas. The artificial convolution neural network acts as a final detection classifier to determine whether a disease pattern is shown on the suspected image area.
3053066
Sensitivity Analysis in Bayesian Networks: From Single to Multiple Parameters
Previous work on sensitivity analysis in Bayesian networks has focused on single parameters, where the goal is to understand the sensitivity of queries to single parameter changes, and to identify single parameter changes that would enforce a certain query constraint. In this paper, we expand the work to multiple parameters which may be in the CPT of a single variable, or the CPTs of multiple variables. Not only do we identify the solution space of multiple parameter changes that would be needed to enforce a query constraint, but we also show how to find the optimal solution, that is, the one which disturbs the current probability distribution the least (with respect to a specific measure of disturbance). We characterize the computational complexity of our new techniques and discuss their applications to developing and debugging Bayesian networks, and to the problem of reasoning about the value (reliability) of new information.
34280688
Mammography Tomosynthesis System for High Performance 3D Imaging
Tomosynthesis provides a major advance in image quality compared to conventional projection mammography by effectively eliminating the effects of superimposed tissue on anatomical structures of interest. Early tomosynthesis systems focused primarily on feasibility assessment by providing 3-dimensional images to determine performance advantages. However, tomosynthesis image quality depends strongly on three key parameters: 1) detector performance at low dose, 2) angular range and number of projections acquired in the tomosynthesis scan, and 3) reconstruction algorithm processing characteristics used to create slice images from the measured projections. In this work, a new GE mammo-graphy tomosynthesis research system was developed that incorporates key improvements in each of these three areas compared to an early feasibility prototype system in use at Massachusetts General Hospital from 2000 to 2004. The performance gains that can be achieved by these enhancements are cha-racterized, and clinical images acquired with the system at the University of Michigan Cancer and Geriatrics Center are presented. The advanced research system also provides the ability to acquire mechanically co-registered x-ray tomosynthesis and ultrasound images of the breast, and initial dual modality images are also presented.