scholarly journals Secure deduplication storage systems supporting keyword search

2015 ◽  
Vol 81 (8) ◽  
pp. 1532-1541 ◽  
Author(s):  
Jin Li ◽  
Xiaofeng Chen ◽  
Fatos Xhafa ◽  
Leonard Barolli
Keyword(s):  
Keyword Search ◽  
Storage Systems

scholarly journals A Study of Practical Proxy Reencryption with a Keyword Search Scheme considering Cloud Storage Structure

2014 ◽  
Vol 2014 ◽  
pp. 1-10
Author(s):  
Sun-Ho Lee ◽  
Im-Yeong Lee
Keyword(s):  
Cloud Storage ◽  
Keyword Search ◽  
Storage Systems ◽  
Searchable Encryption ◽  
Digital Information ◽  
Storage Medium ◽  
Data Outsourcing ◽  
Collusion Attacks ◽  
Storage Limit ◽  
Local Storage

Data outsourcing services have emerged with the increasing use of digital information. They can be used to store data from various devices via networks that are easy to access. Unlike existing removable storage systems, storage outsourcing is available to many users because it has no storage limit and does not require a local storage medium. However, the reliability of storage outsourcing has become an important topic because many users employ it to store large volumes of data. To protect against unethical administrators and attackers, a variety of cryptography systems are used, such as searchable encryption and proxy reencryption. However, existing searchable encryption technology is inconvenient for use in storage outsourcing environments where users upload their data to be shared with others as necessary. In addition, some existing schemes are vulnerable to collusion attacks and have computing cost inefficiencies. In this paper, we analyze existing proxy re-encryption with keyword search.

Institutional Metadata and the Problem of Context

2020 ◽  
Vol 6 (2) ◽  
pp. 17-34
Author(s):  
Jane Birkin
Keyword(s):  
Keyword Search ◽  
Storage Systems ◽  
Contextual Information ◽  
Network Storage ◽  
Visual Content ◽  
Network Culture ◽  
Evidential Value ◽  
Original Order ◽  
Context Free ◽  
Shutter Time

Abstract The traditional archive catalogue constitutes a form of structural and descriptive metadata that long precedes the internet; and the cataloguing of photographs is just one part of a process of archival administration. The application of keywords to images contrasts with archival prose description, which is based on the visual content of the image and is predominantly context-free; a remediation of the image itself. At the heart of this lies the notion that the single photograph is itself devoid of context; it is a discrete embodiment of shutter time and there is nothing certain either side of that. Thus, one can only speculate at its context, and institutional description techniques actively avoid such speculation. Yet context in the archive is ever-present and key to the function of images as objects of information and evidence. It is built through static relationships, through the situating of photographs in accordance with the concept of original order, and it is replicated through storage systems and hierarchical catalogue entries. Such orders, hierarchies and relationships are absent within sets of images that are brought together by keyword search, including through the websites of archival institutions that struggle to reconcile archival principles and identity with network culture. Images are transported to places where contextual information is at best difficult to access, especially for those unfamiliar with archival interfaces. In contrast to the controlled stasis of archival storage and interconnected recordkeeping systems, network storage is messy, unstable and poorly described. However, we must accept that context is not a prerequisite for many users, and for them the networking of archival images denotes a freedom; a democratisation of the archive. But in a media-driven society that is becoming more and more indifferent to the evidential value of documents of any kind, the context-free image is left predisposed to exploitation.

scholarly journals Keyword Search in Decentralized Storage Systems

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2041
Author(s):  
Liyan Zhu ◽  
Chuqiao Xiao ◽  
Xueqing Gong
Keyword(s):  
Data Integration ◽  
Response Time ◽  
Keyword Search ◽  
Storage Systems ◽  
Experimental Results ◽  
Version Control ◽  
Storage Model ◽  
Data Object ◽  
Network Cost ◽  
Merging Algorithms

The emerging decentralized storage systems (DSSs), such as InterPlanetary File System (IPFS), Storj, and Sia, provide people with a new storage model. Instead of being centrally managed, the data are sliced up and distributed across the nodes of the network. Furthermore, each data object is uniquely identified by a cryptographic hash (ObjectId) and can only be retrieved by ObjectId. Compared with the search functions provided by the existing centralized storage systems, the application scenarios of the DSSs are subject to certain restrictions. In this paper, we first apply decentralized B+Tree and HashMap to the DSSs to provide keyword search. Both indexes are kept in blocks. Since these blocks may be scattered on multiple nodes, we ensure that all operations involve as few blocks as possible to reduce network cost and response time. In addition, the version control and version merging algorithms are designed to effectively organize the indexes and facilitate data integration. The experimental results prove that our indexes have excellent availability and scalability.

Creating a standard method of controlling digital microscopes: the microscope access protocol (map)

1995 ◽  
Vol 53 ◽  
pp. 16-17
Author(s):  
T. A. Dodson ◽  
E. Völkl ◽  
L. F. Allard ◽  
T. A. Nolan
Keyword(s):  
Data Storage ◽  
Storage Systems ◽  
Digital Systems ◽  
Data Networks ◽  
Digital Microscopy ◽  
Command Language ◽  
Access Protocol ◽  
Analog To Digital ◽  
Basic Physics ◽  
Scanning Electron

The process of moving to a fully digital microscopy laboratory requires changes in instrumentation, computing hardware, computing software, data storage systems, and data networks, as well as in the operating procedures of each facility. Moving from analog to digital systems in the microscopy laboratory is similar to the instrumentation projects being undertaken in many scientific labs. A central problem of any of these projects is to create the best combination of hardware and software to effectively control the parameters of data collection and then to actually acquire data from the instrument. This problem is particularly acute for the microscopist who wishes to "digitize" the operation of a transmission or scanning electron microscope. Although the basic physics of each type of instrument and the type of data (images & spectra) generated by each are very similar, each manufacturer approaches automation differently. The communications interfaces vary as well as the command language used to control the instrument.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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