44 research outputs found
Conducting Truthful Surveys, Cheaply
We consider the problem of conducting a survey with the goal of obtaining an
unbiased estimator of some population statistic when individuals have unknown
costs (drawn from a known prior) for participating in the survey. Individuals
must be compensated for their participation and are strategic agents, and so
the payment scheme must incentivize truthful behavior. We derive optimal
truthful mechanisms for this problem for the two goals of minimizing the
variance of the estimator given a fixed budget, and minimizing the expected
cost of the survey given a fixed variance goal
Buying Private Data without Verification
We consider the problem of designing a survey to aggregate non-verifiable
information from a privacy-sensitive population: an analyst wants to compute
some aggregate statistic from the private bits held by each member of a
population, but cannot verify the correctness of the bits reported by
participants in his survey. Individuals in the population are strategic agents
with a cost for privacy, \ie, they not only account for the payments they
expect to receive from the mechanism, but also their privacy costs from any
information revealed about them by the mechanism's outcome---the computed
statistic as well as the payments---to determine their utilities. How can the
analyst design payments to obtain an accurate estimate of the population
statistic when individuals strategically decide both whether to participate and
whether to truthfully report their sensitive information?
We design a differentially private peer-prediction mechanism that supports
accurate estimation of the population statistic as a Bayes-Nash equilibrium in
settings where agents have explicit preferences for privacy. The mechanism
requires knowledge of the marginal prior distribution on bits , but does
not need full knowledge of the marginal distribution on the costs ,
instead requiring only an approximate upper bound. Our mechanism guarantees
-differential privacy to each agent against any adversary who can
observe the statistical estimate output by the mechanism, as well as the
payments made to the other agents . Finally, we show that with
slightly more structured assumptions on the privacy cost functions of each
agent, the cost of running the survey goes to as the number of agents
diverges.Comment: Appears in EC 201
Civic Crowdfunding for Agents with Negative Valuations and Agents with Asymmetric Beliefs
In the last decade, civic crowdfunding has proved to be effective in
generating funds for the provision of public projects. However, the existing
literature deals only with citizen's with positive valuation and symmetric
belief towards the project's provision. In this work, we present novel
mechanisms which break these two barriers, i.e., mechanisms which incorporate
negative valuation and asymmetric belief, independently. For negative
valuation, we present a methodology for converting existing mechanisms to
mechanisms that incorporate agents with negative valuations. Particularly, we
adapt existing PPR and PPS mechanisms, to present novel PPRN and PPSN
mechanisms which incentivize strategic agents to contribute to the project
based on their true preference. With respect to asymmetric belief, we propose a
reward scheme Belief Based Reward (BBR) based on Robust Bayesian Truth Serum
mechanism. With BBR, we propose a general mechanism for civic crowdfunding
which incorporates asymmetric agents. We leverage PPR and PPS, to present PPRx
and PPSx. We prove that in PPRx and PPSx, agents with greater belief towards
the project's provision contribute more than agents with lesser belief.
Further, we also show that contributions are such that the project is
provisioned at equilibrium.Comment: Accepted as full paper in IJCAI 201
Redrawing the Boundaries on Purchasing Data from Privacy-Sensitive Individuals
We prove new positive and negative results concerning the existence of
truthful and individually rational mechanisms for purchasing private data from
individuals with unbounded and sensitive privacy preferences. We strengthen the
impossibility results of Ghosh and Roth (EC 2011) by extending it to a much
wider class of privacy valuations. In particular, these include privacy
valuations that are based on ({\epsilon}, {\delta})-differentially private
mechanisms for non-zero {\delta}, ones where the privacy costs are measured in
a per-database manner (rather than taking the worst case), and ones that do not
depend on the payments made to players (which might not be observable to an
adversary). To bypass this impossibility result, we study a natural special
setting where individuals have mono- tonic privacy valuations, which captures
common contexts where certain values for private data are expected to lead to
higher valuations for privacy (e.g. having a particular disease). We give new
mech- anisms that are individually rational for all players with monotonic
privacy valuations, truthful for all players whose privacy valuations are not
too large, and accurate if there are not too many players with too-large
privacy valuations. We also prove matching lower bounds showing that in some
respects our mechanism cannot be improved significantly
A Theory of Pricing Private Data
Personal data has value to both its owner and to institutions who would like
to analyze it. Privacy mechanisms protect the owner's data while releasing to
analysts noisy versions of aggregate query results. But such strict protections
of individual's data have not yet found wide use in practice. Instead, Internet
companies, for example, commonly provide free services in return for valuable
sensitive information from users, which they exploit and sometimes sell to
third parties.
As the awareness of the value of the personal data increases, so has the
drive to compensate the end user for her private information. The idea of
monetizing private data can improve over the narrower view of hiding private
data, since it empowers individuals to control their data through financial
means.
In this paper we propose a theoretical framework for assigning prices to
noisy query answers, as a function of their accuracy, and for dividing the
price amongst data owners who deserve compensation for their loss of privacy.
Our framework adopts and extends key principles from both differential privacy
and query pricing in data markets. We identify essential properties of the
price function and micro-payments, and characterize valid solutions.Comment: 25 pages, 2 figures. Best Paper Award, to appear in the 16th
International Conference on Database Theory (ICDT), 201
Low-Cost Learning via Active Data Procurement
We design mechanisms for online procurement of data held by strategic agents
for machine learning tasks. The challenge is to use past data to actively price
future data and give learning guarantees even when an agent's cost for
revealing her data may depend arbitrarily on the data itself. We achieve this
goal by showing how to convert a large class of no-regret algorithms into
online posted-price and learning mechanisms. Our results in a sense parallel
classic sample complexity guarantees, but with the key resource being money
rather than quantity of data: With a budget constraint , we give robust risk
(predictive error) bounds on the order of . Because we use an
active approach, we can often guarantee to do significantly better by
leveraging correlations between costs and data.
Our algorithms and analysis go through a model of no-regret learning with
arriving pairs (cost, data) and a budget constraint of . Our regret bounds
for this model are on the order of and we give lower bounds on the
same order.Comment: Full version of EC 2015 paper. Color recommended for figures but
nonessential. 36 pages, of which 12 appendi
Keeping The Physical Educator “Connected” An Examination Of Comfort Level, Usage And Professional Development Available For Technology Integration In The Curricular Area Of Physical Education
Schools continue to integrate the use of technology, and gymnasiums are not an exception. The purpose of the study was to determine the comfort level of Physical Education teachers integrating technology in the gymnasium, determine types of professional development provided for technology use, and potential barriers associated with technology usage. A survey of 179 practicing Physical Education teachers located in the Midwest completed an online questionnaire. Results indicated Physical Education teachers were comfortable integrating technology but reported inadequate professional develop on technology device implementation. These findings suggest Physical Educators are willing to integrate technology but the professional development and resources available to accomplish this is lacking. Future research should examine PETE program offerings, and additional PD opportunities offered by SHAPE America within the area of technology and Physical Education