Biblio

Optical Coherence Tomography (OCT) has shown a great potential as a complementary imaging tool in the diagnosis of skin diseases. Speckle noise is the most prominent artifact present in OCT images and could limit the interpretation and detection capabilities. In this work we evaluate various denoising filters with high edge-preserving potential for the reduction of speckle noise in 256 dermatological OCT B-scans. Our results show that the Enhanced Sigma Filter and the Block Matching 3-D (BM3D) as 2D denoising filters and the Wavelet Multiframe algorithm considering adjacent B-scans achieved the best results in terms of the enhancement quality metrics used. Our results suggest that a combination of 2D filtering followed by a wavelet based compounding algorithm may significantly reduce speckle, increasing signal-to-noise and contrast-to-noise ratios, without the need of extra acquisitions of the same frame.

Automated human facial image de-identification is a much needed technology for privacy-preserving social media and intelligent surveillance applications. Other than the usual face blurring techniques, in this work, we propose to achieve facial anonymity by slightly modifying existing facial images into "averaged faces" so that the corresponding identities are difficult to uncover. This approach preserves the aesthesis of the facial images while achieving the goal of privacy protection. In particular, we explore a deep learning-based facial identity-preserving (FIP) features. Unlike conventional face descriptors, the FIP features can significantly reduce intra-identity variances, while maintaining inter-identity distinctions. By suppressing and tinkering FIP features, we achieve the goal of k-anonymity facial image de-identification while preserving desired utilities. Using a face database, we successfully demonstrate that the resulting "averaged faces" will still preserve the aesthesis of the original images while defying facial image identity recognition.

Healthcare technology—sometimes called “healthtech” or “healthsec”—is enmeshed with security and privacy via usability, performance, and cost-effectiveness issues. It is multidisciplinary, distributed, and complex—and it also involves many competing stakeholders and interests. To address the problems that arise in such a multifaceted field—comprised of physicians, IT professionals, management information specialists, computer scientists, edical informaticists, and epidemiologists, to name a few—the Healthtech Declaration was initiated at the most recent USENIX Summit on Information Technologies for Health (Healthtech 2015) held in Washington, DC. This Healthtech Declaration includes an easy-touse—and easy-to-cite—checklist of key issues that anyone proposing a solution must consider (see “The Healthtech Declaration Checklist” sidebar). In this article, we provide the context and motivation for the declaration.

The United States has US CYBERCOM to protect the US Military Infrastructure and DHS to protect the nation's critical cyber infrastructure. These organizations deal with wide ranging issues at a national level. This leaves local and state governments to largely fend for themselves in the cyber frontier. This paper will focus on how to determine the threat to a community and what indications and warnings can lead us to suspect an attack is underway. To try and help answer these questions we utilized the concepts of Honey pots and Honey nets and extended them to a multi-organization concept within a geographic boundary to form a Honey Community. The initial phase of the research done in support of this paper was to create a fictitious community with various components to entice would-be attackers and determine if the use of multiple sectors in a community would aid in the determination of an attack.

Technology’s role in the fight against malicious cyber-attacks is critical to the increasingly networked world of today. Yet, technology does not exist in isolation: the human factor is an aspect of cyber-defense operations with increasingly recognized importance. Thus, the human factors community has a unique responsibility to help create and validate cyber defense systems according to basic principles and design philosophy. Concurrently, the collective science must advance. These goals are not mutually exclusive pursuits: therefore, toward both these ends, this research provides cyber-cognitive links between cyber defense challenges and major human factors and ergonomics (HFE) research areas that offer solutions and instructive paths forward. In each area, there exist cyber research opportunities and realms of core HFE science for exploration. We raise the cyber defense domain up to the HFE community at-large as a sprawling area for scientific discovery and contribution.

With the increasing popularity of wearable devices, information becomes much easily available. However, personal information sharing still poses great challenges because of privacy issues. We propose an idea of Visual Human Signature (VHS) which can represent each person uniquely even captured in different views/poses by wearable cameras. We evaluate the performance of multiple effective modalities for recognizing an identity, including facial appearance, visual patches, facial attributes and clothing attributes. We propose to emphasize significant dimensions and do weighted voting fusion for incorporating the modalities to improve the VHS recognition. By jointly considering multiple modalities, the VHS recognition rate can reach by 51% in frontal images and 48% in the more challenging environment and our approach can surpass the baseline with average fusion by 25% and 16%. We also introduce Multiview Celebrity Identity Dataset (MCID), a new dataset containing hundreds of identities with different view and clothing for comprehensive evaluation.

Today, by widely spread of information technology (IT) usage, E-commerce security and its related legislations are very critical issue in information technology and court law. There is a consensus that security matters are the significant foundation of e-commerce, electronic consumers, and firms' privacy. While e-commerce networks need a policy for security privacy, they should be prepared for a simple consumer friendly infrastructure. Hence it is necessary to review the theoretical models for revision. In This theory review, we embody a number of former articles that cover security of e-commerce and legislation ambit at the individual level by assessing five criteria. Whether data of articles provide an effective strategy for secure-protection challenges in e-commerce and e-consumers. Whether provisions clearly remedy precedents or they need to flourish? This paper focuses on analyzing the former discussion regarding e-commerce security and existence legislation toward cyber-crime activity of e-commerce the article also purports recommendation for subsequent research which is indicate that through secure factors of e-commerce we are able to fill the vacuum of its legislation.

Sharing cyber security data across organizational boundaries brings both privacy risks in the exposure of personal information and data, and organizational risk in disclosing internal information. These risks occur as information leaks in network traffic or logs, and also in queries made across organizations. They are also complicated by the trade-offs in privacy preservation and utility present in anonymization to manage disclosure. In this paper, we define three principles that guide sharing security information across organizations: Least Disclosure, Qualitative Evaluation, and Forward Progress. We then discuss engineering approaches that apply these principles to a distributed security system. Application of these principles can reduce the risk of data exposure and help manage trust requirements for data sharing, helping to meet our goal of balancing privacy, organizational risk, and the ability to better respond to security with shared information.

The quantity of personal data gathered by service providers via our daily activities continues to grow at a rapid pace. The sharing, and the subsequent analysis of, such data can support a wide range of activities, but concerns around privacy often prompt an organization to transform the data to meet certain protection models (e.g., k-anonymity or E-differential privacy). These models, however, are based on simplistic adversarial frameworks, which can lead to both under- and over-protection. For instance, such models often assume that an adversary attacks a protected record exactly once. We introduce a principled approach to explicitly model the attack process as a series of steps. Specically, we engineer a factored Markov decision process (FMDP) to optimally plan an attack from the adversary's perspective and assess the privacy risk accordingly. The FMDP captures the uncertainty in the adversary's belief (e.g., the number of identied individuals that match the de-identified data) and enables the analysis of various real world deterrence mechanisms beyond a traditional protection model, such as a penalty for committing an attack. We present an algorithm to solve the FMDP and illustrate its efficiency by simulating an attack on publicly accessible U.S. census records against a real identied resource of over 500,000 individuals in a voter registry. Our results demonstrate that while traditional privacy models commonly expect an adversary to attack exactly once per record, an optimal attack in our model may involve exploiting none, one, or more indiviuals in the pool of candidates, depending on context.