Nowadays, the cloud computing technology combined with the new generation networks and internet of things facilitate the networking of numerous smart devices. Moreover, the advent of the smart web requires massive data backup from the smart connected devices to the cloud. Unfortunately, the publication of several of these data, such as medical information and financial transactions, could lead to serious privacy breaches, which is becoming the most serious issue in cloud of things. For instance, passive attacks can launched in order to get access to private information. For this reason, several data anonymization techniques have emerged in order to keep data as confidential as possible. However, these different techniques are making the data unusable the most of time. Meanwhile, differential privacy that has been used in a number of cyber physical systems recently emerged as an efficient technique for ensuring the privacy of cloud of things stored data. In this exploratory paper, we study the guarantees of differential privacy of a multi-level anonymization scheme of data graphs. The considered scheme disturbs the structure of the graph by adding false edges, groups the vertices in distinct sets and permutes the vertices in these groups. Particularly, we demonstrated the guarantees that the anonymized data by this algorithm remain exploitable while guaranteeing the anonymity of users.
Mammograms are the images used by radiologists to diagnose breast cancer. In this diagnosis, the pectoral muscle appears in mammograms in oblique mediolateral views (MLO) of the right breast and another in the left breast appears in cranio-caudal views which are marked with (CC). Considering that the pectoral muscle has the same density as the small, suspicious masses in the image, its presence in the image being processed could also require detection procedures. In this paper, we present a new general framework for pectoral muscle suppression which is the first work in the analysis of a mammography image. As a result, we proceed to four stages of image processing. The first step is to orient the image if necessary, then use a pre-processing which is to enhance the contrast of the image, and remove the digital lines of the image by morphological filters, apply a filter median. The third step involves segmenting all of the pectoral muscles, which involves threshold the entire image. The final step is to perform a pectoral muscle removal according to the orientation of the muscle in the image, which will be based on the development of the Hough transform for the recognition of borderline detections of the pectoral muscle. Some results obtained on the different images are discussed and compared with other methods (risk assessments). Evaluation of our method shows a significant improvement in performance in removing the pectoral muscle.
Keywords: Breast cancer, Mammogram, Pectoral muscle, Hough transform.
Considering the evolution of the semantic wiki engine based platforms, two main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis for Ontologies (WfO). OfW vision requires existing ontologies to be imported. Most of them use the RDF-based (Resource Description Framework) systems in conjunction with the standard SQL (Structured Query Language) database to manage and query semantic data. But, relational database is not an ideal type of storage for semantic data. A more natural data model for SMW (Semantic MediaWiki) is RDF, a data format that organizes information in graphs rather than in fixed database tables. This paper presents an ontology based architecture, which aims to implement this idea. The architecture mainly includes three layered functional architectures: Web User Interface Layer, Semantic Layer and Persistence Layer.
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