We were asked to describe the characteristics and differences between the NGT and Delphi models of research. This is my take on the differences, which model I would use and why.
The Classical Delphi model uses the anonymity of the participants allowing them to freely express their opinions without undue social pressures of having to conform to others in the group with decisions bases on merit, rather than who proposes the idea. Whereas, the NGT method limits participants the opportunity to comment on issues that were not covered in surveys and uses focus groups to encourage participants with one or two members that hold strong opinions influencing others and stifling ideas and evaluating factors identified by learners as positive and negative points.
From the readings the NGT method is mainly directed towards the medical field evaluating medical courses. The Delphi method, working with qualitative and quantitative methods seems to be more suited to Information System (IS) research because of its fluidity in its discipline and its flexibility when knowledge about a task is limited.
As for which method I would use from what I have read and think understand, the use of a modified Delphi in a closed or open collaboration would depend on the type of research one wants to accomplish. It seems that a closed collaboration would lead to a specific outcome for a specific study whereas an open collaboration would be used as sort of a brainstorming study to provide a generalized idea or direction in which to proceed.
A Modified Delphi Approach to a New Card Sorting Methodology http://usabilityprofessionals.org/upa_publications/jus/2008november/JUS_Paul_Nov2008.pdf
Tuesday, August 14, 2012
Monday, July 9, 2012
Thursday, February 24, 2011
Privacy preserving cryptographic protocols
This weeks read was about privacy preserving cryptographic protocols.
The common definition of privacy in the cryptographic community limits the information that is leaked by the distributed computation to be the information that can be learned from the designated output of the computation (Benny Pinkas, HP Labs).
This chapter discusses differing protocol frameworks used to achieve this privacy. In Acquisti el al’s:Digital Privacy their definition is a computational function of outputs that are distributed among different participants during online collaboration.
The chapter notes a couple of platforms are in place today that have the goal to provide a privacy-preserving protocol for any possible function:
Secure Multiparty Computation (SMC)
Secure Function Evaluation (SFE)
and goes on to say that it may seem like an impossible task, but general results show that any function that is computable in polynomial time can be computed with polynomial communication.
The chapter goes on to discuss how privacy preserving cryptographic protocols can be applied to differing situations:
Database querying
Distributed voting
Bidding and auctions
Data mining
The Data mining or data warehousing kind of caught my attention because I had to write a white paper on it when it first appeared on the IT table. It was suppose to be the way of the future for storing and locating data quickly and easily. At that time privacy issues were not looked at closely as systems were not as integrated as they are today. Data mining and warehousing fell into the privacy conundrum with the advancements in technology and the widely integrated system structure in place today. It is interesting to see how this is handled by SMC and SFE to provide privacy security.
A couple of approaches were mentioned: Sanitizing Data before making it available and the use of technologies mentioned in the chapter, benchmarking and forecasting, contract negotiations, and rational selfish participants along with the introduction by Lindel-Pinkas method where two parties build a decision tree without either party learning anything about the other.
The common definition of privacy in the cryptographic community limits the information that is leaked by the distributed computation to be the information that can be learned from the designated output of the computation (Benny Pinkas, HP Labs).
This chapter discusses differing protocol frameworks used to achieve this privacy. In Acquisti el al’s:Digital Privacy their definition is a computational function of outputs that are distributed among different participants during online collaboration.
The chapter notes a couple of platforms are in place today that have the goal to provide a privacy-preserving protocol for any possible function:
Secure Multiparty Computation (SMC)
Secure Function Evaluation (SFE)
and goes on to say that it may seem like an impossible task, but general results show that any function that is computable in polynomial time can be computed with polynomial communication.
The chapter goes on to discuss how privacy preserving cryptographic protocols can be applied to differing situations:
Database querying
Distributed voting
Bidding and auctions
Data mining
The Data mining or data warehousing kind of caught my attention because I had to write a white paper on it when it first appeared on the IT table. It was suppose to be the way of the future for storing and locating data quickly and easily. At that time privacy issues were not looked at closely as systems were not as integrated as they are today. Data mining and warehousing fell into the privacy conundrum with the advancements in technology and the widely integrated system structure in place today. It is interesting to see how this is handled by SMC and SFE to provide privacy security.
A couple of approaches were mentioned: Sanitizing Data before making it available and the use of technologies mentioned in the chapter, benchmarking and forecasting, contract negotiations, and rational selfish participants along with the introduction by Lindel-Pinkas method where two parties build a decision tree without either party learning anything about the other.
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