12,620 research outputs found
Outsourcing Back Office Services in Small Nonprofits: Pitfalls and Possibilities
Presents findings on small nonprofits' administrative, finance, and other office support needs; reasons and conditions for outsourcing as well as barriers; methods for evaluating options; and guiding principles. Examines three business models
Privacy in the Genomic Era
Genome sequencing technology has advanced at a rapid pace and it is now
possible to generate highly-detailed genotypes inexpensively. The collection
and analysis of such data has the potential to support various applications,
including personalized medical services. While the benefits of the genomics
revolution are trumpeted by the biomedical community, the increased
availability of such data has major implications for personal privacy; notably
because the genome has certain essential features, which include (but are not
limited to) (i) an association with traits and certain diseases, (ii)
identification capability (e.g., forensics), and (iii) revelation of family
relationships. Moreover, direct-to-consumer DNA testing increases the
likelihood that genome data will be made available in less regulated
environments, such as the Internet and for-profit companies. The problem of
genome data privacy thus resides at the crossroads of computer science,
medicine, and public policy. While the computer scientists have addressed data
privacy for various data types, there has been less attention dedicated to
genomic data. Thus, the goal of this paper is to provide a systematization of
knowledge for the computer science community. In doing so, we address some of
the (sometimes erroneous) beliefs of this field and we report on a survey we
conducted about genome data privacy with biomedical specialists. Then, after
characterizing the genome privacy problem, we review the state-of-the-art
regarding privacy attacks on genomic data and strategies for mitigating such
attacks, as well as contextualizing these attacks from the perspective of
medicine and public policy. This paper concludes with an enumeration of the
challenges for genome data privacy and presents a framework to systematize the
analysis of threats and the design of countermeasures as the field moves
forward
Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing
With the onset of the Information Era and the rapid growth of information
technology, ample space for processing and extracting data has opened up.
However, privacy concerns may stifle expansion throughout this area. The
challenge of reliable mining techniques when transactions disperse across
sources is addressed in this study. This work looks at the prospect of creating
a new set of three algorithms that can obtain maximum privacy, data utility,
and time savings while doing so. This paper proposes a unique double encryption
and Transaction Splitter approach to alter the database to optimize the data
utility and confidentiality tradeoff in the preparation phase. This paper
presents a customized apriori approach for the mining process, which does not
examine the entire database to estimate the support for each attribute.
Existing distributed data solutions have a high encryption complexity and an
insufficient specification of many participants' properties. Proposed solutions
provide increased privacy protection against a variety of attack models.
Furthermore, in terms of communication cycles and processing complexity, it is
much simpler and quicker. Proposed work tests on top of a realworld transaction
database demonstrate that the aim of the proposed method is realistic
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