Money for Nothing and Privacy for Free?

Swapneel Kalpesh Sheth; Tal G. Malkin; Gail E. Kaiser

Money for Nothing and Privacy for Free?
Sheth, Swapneel Kalpesh
Malkin, Tal G.
Kaiser, Gail E.
Technical reports
Computer Science
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Columbia University Computer Science Technical Reports
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Department of Computer Science, Columbia University
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New York
Privacy in the context of ubiquitous social computing systems has become a major concern for the society at large. As the number of online social computing systems that collect user data grows, this privacy threat is further exacerbated. There has been some work (both, recent and older) on addressing these privacy concerns. These approaches typically require extra computational resources, which might be beneficial where privacy is concerned, but when dealing with Green Computing and sustainability, this is not a great option. Spending more computation time results in spending more energy and more resources that make the software system less sustainable. Ideally, what we would like are techniques for designing software systems that address these privacy concerns but which are also sustainable - systems where privacy could be achieved "for free," i.e., without having to spend extra computational effort. In this paper, we describe how privacy can be achieved for free - an accidental and beneficial side effect of doing some existing computation - and what types of privacy threats it can mitigate. More precisely, we describe a "Privacy for Free" design pattern and show its feasibility, sustainability, and utility in building complex social computing systems.
Computer science
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Swapneel Kalpesh Sheth, Tal G. Malkin, Gail E. Kaiser, , Money for Nothing and Privacy for Free?, Columbia University Academic Commons, .

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