Private Databases Research Articles

BLOCK CHAIN BASED EFFICIENT MANAGEMENT OF IOT SMART VIDEO SURVEILLANCE SYSTEM

A management of surveillance of video process which is based on block chain technology has been proposed. The system which has been intended contains a network of block chain with internal nodes which are trusted. The distributed ledger of blockchain includes the metadata of the surveillance footage, thereby stopping the possibility of the data forgery. The architecture of proposed system saves and encrypts the footage, generates a license inside the block chain and exports the footage. Since the private database manages the decryption key for the footage in the block chain, it is not exposed by the internal nodes illegally. Also, the internal administrator can handle and export surveillance footage safely by decrypting the frames stored in the block chain and reconstructing the footage which can be later downloaded.

Upgraded Web Architecture for Mail Security and Customization

Here we are presenting a made sure about DNS with upgraded database which underpins on cloud mail server. DNS in general considered as a straightforward approach where content-based convention occurs, where at least one among the beneficiaries of a message are indicated along with the message content and possibly with additional encoded entities act on behalf of the supreme database. A remote server then receives the message employing a method of inquiries and responses among the client and the server. A server Mail Transfer Agent (MTA) or a Mail User Agent (MUA) whose is the end client can be a customer in the SMTP server database. Here we presenting a technique-based security strategy called as guidance location framework (IDS) which follow the internet protocol (IP) subtleties, date, time and the secret key level of the programmer from the programmer's side. Programmer's area can be discovered utilizing their IP address. The subtleties will be put away in the database from the server side. The email or the DNS client associates with the server MTA through the communication port 25. Telnet program is the most commonly used to test the SMTP server. Upon request DNS doesn't permit one to pull messages from a remote server as it works on the conventional push protocol. With the goal that the primary article is to make protection conservation for the private database the proposed design executes this present reality mysterious database by actualizing the speculation and concealment. It manages forestalling pernicious gatherings and interruption utilizing trust mindful steering system with trust as an assistance. The proficiency and security of information can be accomplished by keeping up single database with explicit access rights. With the activity performed with IDS with ESMTP in Anonymous and Confidential Databases.

Isocitrate dehydrogenase (IDH) status prediction in histopathology images of gliomas using deep learning

Mutations in isocitrate dehydrogenase genes IDH1 and IDH2 are frequently found in diffuse and anaplastic astrocytic and oligodendroglial tumours as well as in secondary glioblastomas. As IDH is a very important prognostic, diagnostic and therapeutic biomarker for glioma, it is of paramount importance to determine its mutational status. The haematoxylin and eosin (H&E) staining is a valuable tool in precision oncology as it guides histopathology-based diagnosis and proceeding patient’s treatment. However, H&E staining alone does not determine the IDH mutational status of a tumour. Deep learning methods applied to MRI data have been demonstrated to be a useful tool in IDH status prediction, however the effectiveness of deep learning on H&E slides in the clinical setting has not been investigated so far. Furthermore, the performance of deep learning methods in medical imaging has been practically limited by small sample sizes currently available. Here we propose a data augmentation method based on the Generative Adversarial Networks (GAN) deep learning methodology, to improve the prediction performance of IDH mutational status using H&E slides. The H&E slides were acquired from 266 grade II-IV glioma patients from a mixture of public and private databases, including 130 IDH-wildtype and 136 IDH-mutant patients. A baseline deep learning model without data augmentation achieved an accuracy of 0.794 (AUC = 0.920). With GAN-based data augmentation, the accuracy of the IDH mutational status prediction was improved to 0.853 (AUC = 0.927) when the 3,000 GAN generated training samples were added to the original training set (24,000 samples). By integrating also patients’ age into the model, the accuracy improved further to 0.882 (AUC = 0.931). Our findings show that deep learning methodology, enhanced by GAN data augmentation, can support physicians in gliomas’ IDH status prediction.

A discussion on the integration of data management systems in ship operations

While the rapid growth of emerging technologies is changing the global maritime industry, maritime organizations have notoriously been conservative and reluctant to make significant changes in their management practice. Data management systems (DMS) have become increasingly important in many other industries, such as health care, shopping, and education and so on. However, ship managers tend to lack the motivation to integrate DMS in ship operations. The related reasons may include inadequate knowledge, poor management practices, and high capital expenditure and so on. In this research, five key hypotheses were developed based on existing knowledge and scholarship. Primary data were gathered through semi-structured interviews and online questionnaires to test these hypotheses. This article draws on data, information, techniques and theories from a variety of sources in both public domain and private databases. It critically examines some key issues of DMS and how it could be used in ship operations to improve management efficiency. Finally, this paper concluded by introducing some recommendations and proposing a model framework for the integration of DMS in ship operations.

Pedigrees and Perpetrators: Uses of DNA and Genealogy in Forensic Investigations.

In the past few years, cases with DNA evidence that could not be solved with direct matches in DNA databases have benefited from comparing single-nucleotide polymorphism data with private and public genomic databases. Using a combination of genome comparisons and traditional genealogical research, investigators can triangulate distant relatives to the contributor of DNA data from a crime scene, ultimately identifying perpetrators of violent crimes. This approach has also been successful in identifying unknown deceased persons and perpetrators of lesser crimes. Such advances are bringing into focus ethical questions on how much access to DNA databases should be granted to law enforcement and how best to empower public genome contributors with control over their data. The necessary policies will take time to develop but can be informed by reflection on the familial searching policies developed for searches of the federal DNA database and considerations of the anonymity and privacy interests of civilians.

Differentially Private Learning with Small Public Data

Differentially private learning tackles tasks where the data are private and the learning process is subject to differential privacy requirements. In real applications, however, some public data are generally available in addition to private data, and it is interesting to consider how to exploit them. In this paper, we study a common situation where a small amount of public data can be used when solving the Empirical Risk Minimization problem over a private database. Specifically, we propose Private-Public Stochastic Gradient Descent, which utilizes such public information to adjust parameters in differentially private stochastic gradient descent and fine-tunes the final result with model reuse. Our method keeps differential privacy for the private database, and empirical study validates its superiority compared with existing approaches.

Enabling Encrypted Boolean Queries in Geographically Distributed Databases

The persistent growth of big data applications has being raising new challenges in managing large volumes of datasets with high scalability, confidentiality protection, and flexible types of search queries. In this paper, we propose a secure design to disassemble the private dataset with the aim to store them across geographically distributed servers while supporting secure multi-client Boolean queries. In this design, the data owner encrypts the private database with the searchable index attributes. The encrypted dataset will be disassembled and distributed evenly across multiple servers by leveraging the property of a distributed index framework. By constructing an encryption structure, generating search tokens, and enabling parallel query, we show how the proposed design performs the secure while efficient Boolean search. These queries are not only limited to those initiated by the data owner but also can be extended to support multiple authorized clients, where each client is allowed to access a necessary part of the private database. In this stage, we advocate a non-interactive authorization scheme where data owner is not required to stay online to process the query request. Moreover, the query operation can be executed in parallel, which significantly improves the search efficiency. We formally characterize the leakage profile, which allow us to follow the existing security analysis method to demonstrate that our system can guarantee data confidentiality and query privacy. To validate our protocol, we implement a system prototype and evaluate the efficiency of our construction. Through experimental results, we demonstrate the effectiveness of our protocol in terms of data outsourcing time and Boolean query time.

Deep learning for screening of interstitial lung disease patterns in high-resolution CT images

To develop a screening tool for the detection of interstitial lung disease (ILD) patterns using a deep-learning method. A fully convolutional network was used for semantic segmentation of several ILD patterns. Improved segmentation of ILD patterns was achieved using multi-scale feature extraction. Dilated convolution was used to maintain the resolution of feature maps and to enlarge the receptive field. The proposed method was evaluated on a publicly available ILD database (MedGIFT) and a private clinical research database. Several metrics, such as success rate, sensitivity, and false positives per section were used for quantitative evaluation of the proposed method. Sections with fibrosis and emphysema were detected with a similar success rate and sensitivity for both databases but the performance of detection was lower for consolidation compared to fibrosis and emphysema. Automatic identification of ILD patterns in a high-resolution computed tomography (CT) image was implemented using a deep-learning framework. Creation of a pre-trained model with natural images and subsequent transfer learning using a particular database gives acceptable results.

Health scores for farmed animals: Screening pig health with register data from public and private databases.

There are growing demands to ensure animal health and, from a broader perspective, animal welfare, especially for farmed animals. In addition to the newly developed welfare assessment protocols, which provide a harmonised method to measure animal health during farm visits, the question has been raised whether data from existing data collections can be used for an assessment without a prior farm visit. Here, we explore the possibilities of developing animal health scores for fattening pig herds using a) official meat inspection results, b) data on antibiotic usage and c) data from the QS (QS Qualität und Sicherheit GmbH) Salmonella monitoring programme in Germany. The objective is to aggregate and combine these register-like data into animal health scores that allow the comparison and benchmark of participating pig farms according to their health status. As the data combined in the scores have different units of measure and are collected in different abattoirs with possibly varying recording practices, we chose a relative scoring approach using z-transformations of different entrance variables. The final results are aggregated scores in which indicators are combined and weighted based on expert opinion according to their biological significance for animal health. Six scores have been developed to describe different focus areas, such as "Respiratory Health", "External Injuries/ Alterations", "Animal Management", "Antibiotic Usage", "Salmonella Status" and "Mortality". These "focus" area scores are finally combined into an "Overall Score". To test the scoring method, existing routine data from 1,747 pig farm units in Germany are used; these farm units are members of the QS Qualität und Sicherheit GmbH (QS) quality system. In addition, the scores are directly validated for 38 farm units. For these farm units, the farmers and their veterinarians provided their perceptions concerning the actual health status and existing health problems. This process allowed a comparison of the scoring results with actual health information using kappa coefficients as a measure of similarity. The score testing of the focus area scores using real information resulted in normalised data. The results of the validation showed satisfactory agreement between the calculated scores for the project farm units and the actual health information provided by the related farmers and veterinarians. In conclusion, the developed scoring method could become a viable benchmark and risk assessment instrument for animal health on a larger scale under the conditions of the German system.

A Survey Research of Satisfaction Levels on Preventing Data Loss and Preserving Privacy

At present in our digital world, data comes and leaves cyberspace at huge rates. A representative organization transfers millions of email messages and downloads, stores, and transmits millions of data sets via various channels on a regular basis. Companies always hold private data of customers, stake holders, industry partners, regulators and they expect them to protect. Unfortunately, today’s industries constantly fall victim to massive data loss, and high-profile data leakages involving sensitive personal and corporate data continue to appear (http://opensecurityfoundation.org). Loss of data could significantly damage a company’s goodwill and reputation and could also invite legal issues or regulatory consequences for negligent security. That’s why, organizations should take measures to manage the sensitive data they carried out, how it’s restricted, and how to prevent the loss from being leaked or compromised. In this respect, over the years the database security community has developed a number of different techniques and approaches to assure data confidentiality, integrity, and availability[14]. Thus data loss prevention and in particular protection of data from unauthorized accesses remain important goal of any data management system. Multi Category Security labeling from a user and system administrator standpoint is straightforward. It consists of configuring a set of categories, which are simply text labels, such as "Company_Confidential" or "Medical_Records", and then assigning users to those categories. The system administrator first configures the categories, then assigns users to them as required. The users can then use the labels as they see fit. A system in a home environment may have only one category of "Private", and be configured so that only trusted local users are assigned to this category. In this paper, we first survey the most relevant concepts underlying the notion of database security, types of losses and summarize the menaces to databases and different categories of vulnerabilities in database. This paper focused on Virtual private database, stops various sensitive data from leaving the corporation’s private confines. This paper illustrates and demonstrates how to enable mutli-level access restrictions which ensures accuracy and security,

Identity Recognition Based on the QRS Complex Dynamics of Electrocardiogram

Biometric identification based on electrocardiogram signals has attracted increasing attention. As the most dominant feature of the electrocardiogram signal, the QRS complex(i.e., the combination of ECG Q, R, and S waves) has been used for identification in some studies. This study aims to investigate the intra-individual stability of the QRS complex dynamics to assess its potential for human identification. The QRS complex dynamics are used as the unique feature to classify the QRS complex, which differs from the time/frequency domain features used in the literature. It is the fundamental feature of the QRS complex and contains the underlying information of the QRS complex. The dynamics of training QRS complexes are extracted and expressed as a constant radial basis function network by using deterministic learning. A set of estimators is constructed to represent the training QRS complexes using constant radial basis function networks. By comparing this set of estimators with the test QRS complex, a set of recognition errors is generated, and the average L 1 norms of the errors are taken as the similarity measure between the dynamics of the training QRS complexes and that of the test QRS complex. Therefore, the test QRS complex can be recognized according to the smallest error principle. The electrocardiogram is classified according to the vote of the test QRS complexes recognition results. A private database and PTB diagnostic ECG database are used to test the proposed method. Experimental results on the private database (PTB database) showed that the average identification accuracy was 96.12% (97.42%) for 5-fold cross-validation based on one-lead electrocardiogram and 99.50% (99.23%) for 2-fold cross-validation based on two-lead electrocardiogram, respectively. These show that the dynamics of the QRS complex are well-differentiated for different individuals.

Fair Data Transactions Across Private Databases

In this era of big data, data transactions are becoming more frequent and bringing new benefits for both data buyers and providers. However, there are no data transaction schemes to satisfy the special security and business requirements of data transactions. In this paper, we introduce the requirements in practical data transaction processes and compare the differences of requirements and techniques between data transaction and traditional data sharing. Next, we propose and implement the first data transaction scheme based on a newly defined concept probabilistic commutative encryption, which makes the decryption order of a probabilistic encryption algorithm and a deterministic encryption algorithm commutative. Since the security concerns of both sides can be achieved in a malicious model, our scheme is fair to both data buyers and data providers from a business perspective. Furthermore, our scheme allows data transaction to be partly outsourced to a semi-honest cloud storage platform.

Recent Techniques and Trends for Retinal Blood Vessel Extraction and Tortuosity Evaluation: A Comprehensive Review

Retinal blood vessel segmentation plays an important part in the early diagnosis and treatment of eye disease. It is a tool for ophthalmologists. Many diseases can be identified by examining manifestations and images of blood vessels, including diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinopathy due to hypertension, glaucoma and others. Early detection allows physicians to provide patients with effective treatment, while in the opposite case, the late detection of retinal disease can ultimately lead to blindness. One of the indices when examining the retina is an evaluation of blood vessels based on tortuosity, i.e. the degree of curvature of blood vessels. This article presents a comprehensive overview of all segmentation techniques for retinal blood vessel extraction from images taken with a fundus camera in adults and older children or with a RetCam fundus camera in new-borns and younger children over the last 10 years. An integral part of this review is a comprehensive overview with information on all available public and private databases with retinal images. The review includes an evaluation of segmentation techniques based on objectivization parameters, including information on all objectivization parameters used in this article. As already mentioned, the degree of curvature of retinal blood vessels is used to classify severity of blood vessels tortuosity. There is no uniform metric for determining tortuosity, but this review presents a comprehensive overview of all metrics and calculations used to determine the degree of tortuosity of retinal blood vessels.

Lucene-P2: A Distributed Platform for Privacy-Preserving Text-based Search.

Information retrieval (IR) plays an essential role in daily life. However, currently deployed IR technologies, e.g., Apache Lucene - open-source search software, are insufficient when the information is protected or deemed to be private. For example, submitting a query to a publicly available search engine (e.g., Bing or Google) requires disclosing potentially delicate facts (e.g., thoughts about abortion), as well as the websites the user considers interesting. Similarly, when a private database contains sensitive information needed by the user, it cannot be searched freely. Over the past decade, various approaches, generally referred to as private information retrieval, have been proposed to obfuscate queries and responses, but they are limited in that the retrieved information is inadequate to compute relevancy. To address such limitations, this paper introduces the necessary techniques to build Lucene-P2 that allows one party to discover whether a second party harbors any relevant textual information without either party disclosing any information.

Security improvements of several basic quantum private query protocols with O(log N) communication complexity

New quantum private database (with N elements) query protocols are presented and analyzed. Protocols preserve O(log⁡N) communication complexity of known protocols for the same task, but achieve several significant improvements in security, especially concerning user privacy. For example, the randomized form of our protocol has a cheat-sensitive property – it allows the user to detect a dishonest database with a nonzero probability, while the phase-encoded private query protocols [6,7] for the same task do not have such a property. Moreover, when the database performs the computational basis measurement, a particular projective measurement which can cause a significant loss of user privacy in the previous private query protocols with O(log⁡N) communication complexity, at most half of the user privacy could leak to such a database in our protocol, while in the QPQ protocol [5], the entire user privacy could leak out. In addition, it is proved here that for large N, the user could detect a cheating via the computational basis measurement, with a probability close to 12 using O(N) special queries. Finally, it is shown here, for both forms of our protocol, basic and randomized, how a dishonest database has to act in case it could not learn user's queries.

Efficient private database queries using ring-LWE somewhat homomorphic encryption

We study the problem of private database queries over any outsourced encrypted database. In 2016, Kim et al. [IEEE Trans. on Dependable and Secure Comput.] showed three private query processing protocols for conjunctive, disjunctive, and threshold queries respectively over an encrypted database. First, we propose two more efficient protocols of processing conjunctive and disjunctive queries with a lower-depth equality circuit than Kim et al.’s one. To get the lower-depth circuit, we modify the packing methods of Saha and Koshiba [APWConCSE 2016] to support an efficient batch computation of our protocols within a few multiplications using binary encoded data. Secondly, we propose another efficient protocol engaging a batch technique for threshold query processing in which many inequality comparisons are indispensable. Finally, we propose the packing methods to support batch computation of many inequality circuits. In addition, our theoretical analysis along with practical experiments show that our protocols are more efficient than any other existing method. We further enhance the performance of conjunctive and disjunctive query protocols using a base-N fixed-length encoding.

Detection of Blast Cells in Microscopic Images Using Optimization Algorithms

Now a day’s in medical field, the most prevalent and hazardous disease is blood cancer. It starts in bone marrow where the blood is produced and prevents many of its regular functions. At an approximation for every three minutes one person in the world is diagnosed with the blood cancer. Early detection of the cancer is necessary for the proper treatment, so as to save the lives of mankind. In general the blood cancer diagnosis will be performed by visual examination of the blood samples of the patient under microscope. The blood cancer detection accuracy of this method depends on the technical skilling abilities of the operator and often leads non-standardized report. To improve patient diagnosis various image processing methods are developed to extract useful information from microscopic images. This could help Hematologists in their diagnostic process. In this paper pre-processing and post-processing methods are applied on microscopic images. These images will be acquired from either public or private database. In this work the images were collected from Microscopyu which is a public database. Pre-processing methods involves color conversion i.e. RGB to grayscale, removal of noise by median filter and an improved contrast enhancement technique CLAHE is implemented. Later Post-processing methods are applied. In this stage Otsu segmentation and optimization algorithms are combined for improving segmentation accuracy. Optimization algorithms used are Particle Swarm optimization (PSO) and Cuckoo Search algorithms (CSO) and Finally features of the segmented image can be extracted from d Scale Invariant Feature Transform (SIFT) . In this work existing method is PSO and proposed method is CSO. At the end the qualitative analysis of the work is done through the statistical parameters like segmentation accuracy, sensitivity, specificity, PSNR and CPU time.

Next Generation Sequencing Data and its Compression

Over the past few years the amount of digital memory and network traffic used by sequenced biological data has increased dramatically. Genomic projects such as HapMap, 1000 Genomes, etc., have come to the collection and description of genomes of 2,504 individuals from 26 populations, and they contributed to exponential growth of databases of this type and to the development of increasingly efficient technologies. Thanks to the large-scale sequencing of samples of DNA, the interest and the new research in these areas by the scientific community are suddenly grown. In a very short time researchers have developed hardware tools, analysis software, algorithms, private databases and infrastructures to support genomics. In this paper we analyse different approaches for compressing digital files generated by Next-Generation Sequencing tools containing nucleotide sequences and we discuss and evaluate the compression performance of generic compression tools such as gzip and bzip2 by confronting them with a specific system that was designed specifically for genomic file compression: quip.

Fourth Amendment Protections of Health Information After Carpenter v. United States: The Devil's In The Database.

Every day, companies collect health information from customers and analyze it for commercial purposes. This poses a significant threat to privacy, particularly as the Fourth Amendment protection of this deeply personal information is limited. Generally, law enforcement officers do not need probable cause and a warrant to access these private health information databases; only a subpoena is required, and sometimes nothing at all. The Fourth Amendment protections for health information may, however, have changed after the Supreme Court's 2018 decision in Carpenter v. United States, which held that the Fourth Amendment protects people from warrantless searches of historical cell-site location information possessed by their cell phone providers. The Court explained that, because of the nature of historical cell-site location information, individuals retain a reasonable expectation of privacy despite the information being in the possession of a third party. In reaching its holding, the Supreme Court considered the type of data, the uniqueness of cell-site location information, the impact of technological advancement on privacy, the voluntariness of the disclosure, and the property rights associated with the records. Many of these factors could support heightened Fourth Amendment protection for health information. This Article argues that Carpenter v. United States provides additional protections for future searches of health information in private databases.

2337. Health Outcomes in Congenital Cytomegalovirus, a Systematized and Unbiased Approach in the Electronic Medical Record Era

BackgroundThere is limited data on the indirect and non-medical costs associated with congenital cytomegalovirus (cCMV). Attempts to predict the economic impact of disease often rely on secondary analyses of large private databases, and may not capture the full spectrum of a disease. The granularity of billing codes in the Electronic Medical Record (EMR) make it possible to track health outcomes over time, however, with over 80,000 unique codes in ICD-10, selecting the appropriate codes requires specific content knowledge and can lead to bias in categorization. The Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT)® provides physicians a tool to find specific ICD-10 on the basis of semantic terms. These terms can be used to build disease state-specific clusters of ICD-10 codes by which to study the economic impact of any disease, including this potentially devastating congenital infection.MethodsUsing a series of data parsing and processing scripts written in SAS V9.4 (Cary, NC), we extracted the diagnosis codes for 190 patients seen in our Congenital Cytomegalovirus Clinic at Texas Children’s Hospital in Houston, Texas. This data were consolidated into a relational database of clinical information. Through a second program we developed, clusters of ICD-10 codes were imputed from the SNOMED-CT® on the basis of semantic terms associated with cCMV (e.g., “hearing problem,” “developmental disability,” “neurological problem”).ResultsA total of 190 patients have been seen in our clinic with an ICD-10 diagnosis of CMV infection, 144 of these had cCMV, and 102 of these were born after 1/1/2008 (the inception date of our EMR). 60% of these patients were Caucasian (21% Hispanic), and 25% African American. 54 (53%) had hearing deficits, 17 (16%) had hearing aids, and 55 (54%) had developmental abnormalities. The average time (in years) to development of specific deficits are shown in Figure 1.ConclusionThe spectrum of disease of cCMV is broad and has been well studied in the past. The EMR gives us the potential to further study this disease in finer detail and identify rates of disease progression by mining the ICD-10 codes associated with these patients throughout time. These results should prove invaluable for generating cost-models for the economic impact of cCMV. Disclosures All authors: No reported disclosures.