Secure Deduplication for Cloud Storage Using Interactive Message-Locked Encryption with Convergent Encryption, To Reduce Storage Space

ABSTRACT The digital data stored in the cloud requires much space due to copy of the same data. It can be reduced by dedupilcation, eliminating the copy of the repeated data in the cloud provided services. Identifying common checkoff data both files storing them only once. Deduplication can yield cost savings by increasing the utility of a given amount of storage. Unfortunately, deduplication has many security problems so more than one encryption is required to authenticate data. We have developed a solution that provides both data security and space efficiency in server storage and distributed content checksum storage systems. Here we adopt a method called interactive Message-Locked Encryption with Convergent Encryption (iMLEwCE). In this iMLEwCE the data is encrypted firstly then the cipher text is again encrypted. Block-level deduplication is used to reduce the storage space. Encryption keys are generated in a consistent configuration of data dependency from the chunk data. The identical chunks will always encrypt to the same cipher text. The keys configuration cannot be deduced by the hacker from the encrypted chunk data. So the information is protected from cloud server. This paper focuses on reducing the storage space and providing security in online cloud deduplication.

Medical Images Compression for Region of Interest Based on Curvelet Transform and SPIHT Algorithm / 中国医学影像学杂志

Purpose To propose a novel compression method for region of interest (ROI) based on Curvelet transform and SPIHT algorithm. Materials and Methods The ROI was firstly extracted without compression, and Curvelet transform was applied for the background regions. The Curvelet coefifcients were coded using SPIHT algorithm. Then the images after compression are obtained by inverse Curvelet transform. The ROI and the background were ifnally overlapped to get the full compressed image. Effect of ROI compression and overall compression were compared, as well as the Curvelet transform and wavelet transform, based on peak signal noise ratio. Results The ROI compression highlighted the region of interest and the visual effect was superior to the overall compression. The peak signal to noise of Curvelet transform was higher than that of wavelet transforms, and the compressed images were more clear for the same proportion. Conclusion ROI compression based on Curvelet transform and SPIHT algorithm can achieve efficient compression images without losing important diagnostic information, which complies with the requirement of high precision and high quality of medical image compression.

Compresión de imágenes médicas / Medical image compression: a review

La medicina moderna es una actividad cada vez más compleja, basada en la información proveniente de múltiples fuentes: historias clínicas, dictáfonos e imágenes y vídeos provenientes de múltiples dispositivos. Las imágenes médicas constituyen una de las fuentes de mayor importancia, por cuanto ofrecen un apoyo integral del acto médico: el diagnóstico y el seguimiento. Sin embargo, la cantidad de información generada por los dispositivos de adquisición de imágenes sobrepasa rápidamente la disponibilidad de almacenamiento que tienen los servicios de radiología, lo cual genera costos adicionales en equipos de cómputo con mayor capacidad de almacenamiento. Además, la tendencia actual de desarrollo de aplicaciones en la "nube de cómputo", tiene limitaciones por cuanto, aunque el almacenamiento es virtual y está disponible desde cualquier sitio, la conexión se hace a través de internet. En estos dos casos, el uso óptimo de la información requiere necesariamente de algoritmos de compresión potentes y adaptados a las necesidades de la actividad médica. En este artículo se presenta una revisión de las técnicas de compresión más utilizadas para el almacenamiento de imágenes, así como un análisis crítico de éstas desde el punto de vista de su uso en ambientes clínicos.

Modern medicine is an increasingly complex activity , based on the evidence ; it consists of information from multiple sources : medical record text , sound recordings , images and videos generated by a large number of devices . Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up . However , the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services , generating additional costs in devices with greater storage capacity . Besides , the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet . In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs . In this paper we present a review of compression techniques used for image storage , and a critical analysis of them from the point of view of their use in clinical settings.

BIND – An algorithm for loss-less compression of nucleotide sequence data.

Recent advances in DNA sequencing technologies have enabled the current generation of life science researchers to probe deeper into the genomic blueprint. The amount of data generated by these technologies has been increasing exponentially since the last decade. Storage, archival and dissemination of such huge data sets require efficient solutions, both from the hardware as well as software perspective. The present paper describes BIND – an algorithm specialized for compressing nucleotide sequence data. By adopting a unique ‘block-length’ encoding for representing binary data (as a key step), BIND achieves significant compression gains as compared to the widely used general purpose compression algorithms (gzip, bzip2 and lzma). Moreover, in contrast to implementations of existing specialized genomic compression approaches, the implementation of BIND is enabled to handle non-ATGC and lowercase characters. This makes BIND a loss-less compression approach that is suitable for practical use. More importantly, validation results of BIND (with real-world data sets) indicate reasonable speeds of compression and decompression that can be achieved with minimal processor/ memory usage. BIND is available for download at http://metagenomics.atc.tcs.com/compression/BIND. No license is required for academic or non-profit use.

Evaluation of Mammographic Phantom Images Using JPEG2000 Image Compression

PURPOSE: To determine the usefulness of compression standard JPEG2000 for compression of mammographic images. MATERIALS AND METHODS: Image of a mammographic phantom was compressed using JPEG2000 at ratios of 10:1, 20:1, 30:1, 40:1, 50:1 and 60:1. The sizes of the images were compared, and scores were recorded by counting the numbers of fibers, groups of specks and masses seen in each phantom image. More than four fibers, three groups of specks and three masses and a total score of 10 were considered acceptable. RESULTS: The size of a DICOM image was 17,042 KB, a TIFF image was 8,324 KB, the original JPEG image was 1,506 KB and the most compressed image (50:1) above an acceptable total score of 10 was 43 KB. In each category, the compression image of fiber was acceptable up to compression ratio of 50:1 (score of 5), groups of specks was acceptable up to 60:1 (score of 3) and mass was acceptable up to 50:1 (score of 3.5). The total score, which was acquired by adding up the individual scores of all three categories, for a compression ratio of 50:1 was 12 and was acceptable, but the total score for 60:1 was 8 and was not acceptable. CONCLUSION: The compression standard JPEG2000 is an efficient means for compressing mammographic images at high ratios without compromising diagnostic value.

Clinical Evaluation of the JPEG2000 Compression Rate of CT and MR Images for Long Term Archiving in PACS

PURPOSE: We wanted to evaluate an acceptable compression rate of JPEG2000 for long term archiving of CT and MR images in PACS. MATERIALS AND METHODS: Nine CT images and 9 MR images that had small or minimal lesions were randomly selected from the PACS at our institute. All the images are compressed with rates of 5:1, 10:1, 20:1, 40:1 and 80:1 by the JPEG2000 compression protocol. Pairs of original and compressed images were compared by 9 radiologists who were working independently. We designed a JPEG2000 viewing program for comparing two images on one monitor system for performing easy and quick evaluation. All the observers performed the comparison study twice on 5 mega pixel grey scale LCD monitors and 2 mega pixel color LCD monitors, rspectively. The PSNR (Peak Signal to Noise Ratio) values were calculated for making quantitative comparisons. RESULTS: On MR and CT, all the images with 5:1 compression images showed no difference from the original images by all 9 observers and only one observer could detect a image difference on one CT image for 10:1 compression on only the 5 mega pixel monitor. For the 20:1 compression rate, clinically significant image deterioration was found in 50% of the images on the 5M pixel monitor study, and in 30% of the images on the 2M pixel monitor. PSNR values larger than 44 dB were calculated for all the compressed images. CONCLUSION: The clinically acceptable image compression rate for long term archiving by the JPEG2000 compression protocol is 10:1 for MR and CT, and if this is applied to PACS, it would reduce the cost and responsibility of the system.

A study on ECG Data Compression Method Based on BP Neural Network / 医疗卫生装备

Objective Aimed at the problems that the costs of existing ECG data compression methods is high,and they are difficult to apply in engineering practice,a sort BP neural network is set up based on ECG data compression method.Methods Based on BP network theory,two three-layered feedforward neural networks were set up.Then every one heartbeat was divided into three waves,that is,P,QRS and T ones,and the three waves were compressed by two three-layered feedforward neural network individually.In order to improve the replay capability and interference rejection capability of the neural network compress algorithm,incompletely connected structure is employed.Results The method could realize high compress ratio,and improve the replay capability and interference rejection capability of the heartbeat waves.Conclusion Upon with the heartbeat signals,the method can filter and compress waves effectively,and can be used in engineering practice as well.

The Development and Application of Emergency Teleradiology(Medical Image Transmission) System with Personal Computer

In the clinical practice of neurosurgery, there are frequent needs which require urgent communication between housestaff and attending physician due to the emergency situation. But, the traditional methods such as telephone communication were not sufficient and sometimes, result in misjudgement of patient's status. So, our research team develop the computer software which can be used for emergency surgical and medical decision making. This software can transmitted the high quality images of CT, MRI and other X-ray with the conventional telephone line and personal computer system.

Research and Realization of ECG Data Lossless Compression Based on MCU / 医疗卫生装备

Objective To develop a lossless compression algorithm applied on the MCU.Methods Direct difference data compression and 2-4-8 three-word-length coding data compression based on the first order difference were developed.Statistics on the results of the first order difference of ECG data in the MIT_BIH ECG database resulted in an optimal three-word-length code table.Results Direct difference data compression ratio was lower than 2,and three-word-length data compression ratio was around 3.Conclusion There are both advantages and disadvantages in two types of lossless data compression,but three-word-length data compression is superior to direct difference data compression.[Chinese Medical Equipment Journal,2008,29(2):24-27]