From e35250d619d2fd4f59c26cce7a6cffef213d3058 Mon Sep 17 00:00:00 2001
From: Stratis Ioannidis <stratis@stratis-Latitude-E6320.(none)>
Date: Mon, 5 Nov 2012 01:46:52 -0800
Subject: related

---
 related.tex | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

(limited to 'related.tex')

diff --git a/related.tex b/related.tex
index 2788478..f61abc4 100644
--- a/related.tex
+++ b/related.tex
@@ -13,7 +13,16 @@ a truthful, $O(\log^3 n)$-approximate mechanism
 \cite{dobz2011-mechanisms} as well as a a universally truthful, $O(\frac{\log n}{\log \log n})$-approximate mechanism for subadditive maximization. 
 \cite{bei2012budget}. Moreover, in a Bayesian setup, assuming a prior distribution among the agent's costs, there exists a truthful mechanism with a 768/512-approximation ratio \cite{bei2012budget}. %(in terms of expectations)
 
-\stratis{TODO: privacy discussion. Logdet objective. Should be one paragraph each.}
+ A series of recent papers  \cite{mcsherrytalwar,approximatemechanismdesign,xiao:privacy-truthfulness,chen:privacy-truthfulness} consider the related problem of conducting retreive data from an  \textit{unverified} database: the buyer of the data cannot verify the data reported by individuals and therefore must incentivize them to report truthfully. 
+McSherry and Talwar \cite{mcsherrytalwar} argue that \emph{differentially private} mechanisms also offer a form of \emph{approximate truthfulness}, as the mechanism's output is only slightly purturbed when an individual unilaterally changes her data value; as a result, reporting untruthfully can only increase an individual's utility by a small amount. Xiao \cite{xiao:privacy-truthfulness}, improving upon earlier work by Nissim et al.~\cite{approximatemechanismdesign} construct mechanisms that
+simultaneously achieve exact truthfulness as well as differential privacy. Eliciting private data through a \emph{survey} \cite{roth-liggett}, whereby individuals first decide whether to participate in the survey and then report their data,
+ also fall under the unverified database setting \cite{xiao:privacy-truthfulness}. Our work differs from the above works in that it does not capture user utility through differential privacy; crucially, we also assume that experiments conducted are \emph{tamper-proof}, and individuals can missreport their costs but not their values.
+
+Our work is closest to Roth and Schoenebeck \cite{roth-shoenebeck}, who also consider how to sample individuals from a population to obtain an unbiased estimate of a reported value. The authors assume a prior on the joint distribution
+\stratis{to be continued}
+
+
+%\stratis{TODO: privacy discussion. Logdet objective. Should be one paragraph each.}
 
 \begin{comment}
 Two types of mechanisms: \emph{deterministic} and \emph{randomized}. For
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