Data access

About: Data access is a research topic. Over the lifetime, 13141 publications have been published within this topic receiving 172859 citations. The topic is also known as: Data access.

Time-Based Proxy Re-encryption Scheme for Secure Data Sharing in a Cloud Environment

Abstract: In this research paper, we will describe the system model and security model in our scheme and provide our design goals and related assumptions. We consider a cloud computing environment consisting of a cloud service provider (CSP), a data owner, and many users. The CSP maintains cloud infrastructures, which pool the bandwidth, storage space, and CPU power of many cloud servers to provide 24/7 services. We assume that the cloud infrastructures are more reliable and powerful than personal computers. In our system, the CSP mainly provides two services: data storage and re-encryption. After obtaining the encrypted data from the data owner, the CSP will store the data on several cloud servers, which can be chosen by the consistent hash function, where the input of the consistent hash function is the key of the data, and the outputs of the consistent hash function are the IDs of the servers that store the data. On receiving a data access request from a user, the CSP will re-encrypt the cipher text based on its own time, and return the re-encrypted cipher text.

A framework for scientific data modeling and automated software development

Abstract: Motivation: The lack of standards for storage and exchange of data is a serious hindrance for the large-scale data deposition, data mining and program interoperability that is becoming increasingly important in bioinformatics. The problem lies not only in defining and maintaining the standards, but also in convincing scientists and application programmers with a wide variety of backgrounds and interests to adhere to them. Results: We present a UML-based programming framework for the modeling of data and the automated production of software to manipulate that data. Our approach allows one to make an abstract description of the structure of the data used in a particular scientific field and then use it to generate fully functional computer code for data access and input/output routines for data storage, together with accompanying documentation. This code can be generated simultaneously for different programming languages from a single model, together with, for example for format descriptions and I/O libraries XML and various relational databases. The framework is entirely general and could be applied in any subject area. We have used this approach to generate a data exchange standard for structural biology and analysis software for macromolecular NMR spectroscopy. Availability: The framework is available under the GPL license, the data exchange standard with generated subroutine libraries under the LGPL license. Both may be found at http://www.ccpn.ac.uk; http://sourceforge.net/projects/ccpn Contact: ccpn@mole.bio.cam.ac.uk

Sharing privacy-sensitive access to neuroimaging and genetics data: a review and preliminary validation

Abstract: The growth of data sharing initiatives for neuroimaging and genomics represents an exciting opportunity to confront the ``small $N$'' problem that plagues contemporary neuroimaging studies while further understanding the role genetic markers play in in the function of the brain. When it is possible, open data sharing provides the most benefits. However some data cannot be shared at all due to privacy concerns and/or risk of re-identification. Sharing other data sets is hampered by the proliferation of complex data use agreements (DUAs) which preclude truly automated data mining. These DUAs arise because of concerns about the privacy and confidentiality for subjects; though many do permit direct access to data, they often require a cumbersome approval process that can take months. An alternative approach is to only share data derivatives such as statistical summaries -- the challenges here are to reformulate computational methods to quantify the privacy risks associated with sharing the results of those computations. For example, a derived map of gray matter is often as identifiable as a fingerprint. Thus alternative approaches to accessing data are needed. This paper reviews the relevant literature on differential privacy, a framework for measuring and tracking privacy loss in these settings, and demonstrates the feasibility of using this framework to calculate statistics on data distributed at many sites while still providing privacy.

Adaptive Privacy-Preserving Visualization Using Parallel Coordinates

Abstract: Current information visualization techniques assume unrestricted access to data. However, privacy protection is a key issue for a lot of real-world data analyses. Corporate data, medical records, etc. are rich in analytical value but cannot be shared without first going through a transformation step where explicit identifiers are removed and the data is sanitized. Researchers in the field of data mining have proposed different techniques over the years for privacy-preserving data publishing and subsequent mining techniques on such sanitized data. A well-known drawback in these methods is that for even a small guarantee of privacy, the utility of the datasets is greatly reduced. In this paper, we propose an adaptive technique for privacy preser vation in parallel coordinates. Based on knowledge about the sensitivity of the data, we compute a clustered representation on the fly, which allows the user to explore the data without breaching privacy. Through the use of screen-space privacy metrics, the technique adapts to the user's screen parameters and interaction. We demonstrate our method in a case study and discuss potential attack scenarios.

Data Access Patterns: Database Interactions in Object-Oriented Applications

Abstract: From the Publisher: 25 proven patterns for improving data access and application performance Efficient, high-quality data access code is crucial to the performance and usability of virtually any enterprise application--and there's no better way to improve an existing system than to optimize its data access code. Regardless of database engine, platform, language, orapplication, developers repeatedly encounter the same relational database access challenges. In Data Access Patterns, Clifton Nock identifies 25 proven solutions, presenting each one in the form of a clear, easy-to-use pattern. These patterns solve an exceptionally wide range of problems including creating efficient database-independent applications, hiding obscure database semantics from users, speeding database resource initialization, simplifying development and maintenance, improving support for concurrency and transactions, and eliminating data access bottlenecks. Every pattern is illustrated with fully commented Java/JDBC code examples, as well as UML diagrams representing interfaces, classes, and relationships. The patterns are organized into five categories: Decoupling Patterns: Build cleaner, more reliable systems by decoupling data access code from other application logic Resource Patterns: Manage relational database resources more efficiently Input/Output Patterns: Simplify I/O operations by translating consistently between "physical" relational data and domain object representations of that data Cache Patterns: Use caching strategically, to optimize the tradeoffs between data access optimization and cache overhead Concurrency Patterns: Implement concurrencyand transactions more effectively and reliably Data Access Patterns demystifies techniques that have traditionally been used only in the most robust data access solutions--making those techniques practical for every software developer, architect, and designer.