8,926 research outputs found
How to Balance Privacy and Money through Pricing Mechanism in Personal Data Market
A personal data market is a platform including three participants: data
owners (individuals), data buyers and market maker. Data owners who provide
personal data are compensated according to their privacy loss. Data buyers can
submit a query and pay for the result according to their desired accuracy.
Market maker coordinates between data owner and buyer. This framework has been
previously studied based on differential privacy. However, the previous study
assumes data owners can accept any level of privacy loss and data buyers can
conduct the transaction without regard to the financial budget. In this paper,
we propose a practical personal data trading framework that is able to strike a
balance between money and privacy. In order to gain insights on user
preferences, we first conducted an online survey on human attitude to- ward
privacy and interest in personal data trading. Second, we identify the 5 key
principles of personal data market, which is important for designing a
reasonable trading frame- work and pricing mechanism. Third, we propose a
reason- able trading framework for personal data which provides an overview of
how the data is traded. Fourth, we propose a balanced pricing mechanism which
computes the query price for data buyers and compensation for data owners
(whose data are utilized) as a function of their privacy loss. The main goal is
to ensure a fair trading for both parties. Finally, we will conduct an
experiment to evaluate the output of our proposed pricing mechanism in
comparison with other previously proposed mechanism
Privacy and Truthful Equilibrium Selection for Aggregative Games
We study a very general class of games --- multi-dimensional aggregative
games --- which in particular generalize both anonymous games and weighted
congestion games. For any such game that is also large, we solve the
equilibrium selection problem in a strong sense. In particular, we give an
efficient weak mediator: a mechanism which has only the power to listen to
reported types and provide non-binding suggested actions, such that (a) it is
an asymptotic Nash equilibrium for every player to truthfully report their type
to the mediator, and then follow its suggested action; and (b) that when
players do so, they end up coordinating on a particular asymptotic pure
strategy Nash equilibrium of the induced complete information game. In fact,
truthful reporting is an ex-post Nash equilibrium of the mediated game, so our
solution applies even in settings of incomplete information, and even when
player types are arbitrary or worst-case (i.e. not drawn from a common prior).
We achieve this by giving an efficient differentially private algorithm for
computing a Nash equilibrium in such games. The rates of convergence to
equilibrium in all of our results are inverse polynomial in the number of
players . We also apply our main results to a multi-dimensional market game.
Our results can be viewed as giving, for a rich class of games, a more robust
version of the Revelation Principle, in that we work with weaker informational
assumptions (no common prior), yet provide a stronger solution concept (ex-post
Nash versus Bayes Nash equilibrium). In comparison to previous work, our main
conceptual contribution is showing that weak mediators are a game theoretic
object that exist in a wide variety of games -- previously, they were only
known to exist in traffic routing games
Independent Set, Induced Matching, and Pricing: Connections and Tight (Subexponential Time) Approximation Hardnesses
We present a series of almost settled inapproximability results for three
fundamental problems. The first in our series is the subexponential-time
inapproximability of the maximum independent set problem, a question studied in
the area of parameterized complexity. The second is the hardness of
approximating the maximum induced matching problem on bounded-degree bipartite
graphs. The last in our series is the tight hardness of approximating the
k-hypergraph pricing problem, a fundamental problem arising from the area of
algorithmic game theory. In particular, assuming the Exponential Time
Hypothesis, our two main results are:
- For any r larger than some constant, any r-approximation algorithm for the
maximum independent set problem must run in at least
2^{n^{1-\epsilon}/r^{1+\epsilon}} time. This nearly matches the upper bound of
2^{n/r} (Cygan et al., 2008). It also improves some hardness results in the
domain of parameterized complexity (e.g., Escoffier et al., 2012 and Chitnis et
al., 2013)
- For any k larger than some constant, there is no polynomial time min
(k^{1-\epsilon}, n^{1/2-\epsilon})-approximation algorithm for the k-hypergraph
pricing problem, where n is the number of vertices in an input graph. This
almost matches the upper bound of min (O(k), \tilde O(\sqrt{n})) (by Balcan and
Blum, 2007 and an algorithm in this paper).
We note an interesting fact that, in contrast to n^{1/2-\epsilon} hardness
for polynomial-time algorithms, the k-hypergraph pricing problem admits
n^{\delta} approximation for any \delta >0 in quasi-polynomial time. This puts
this problem in a rare approximability class in which approximability
thresholds can be improved significantly by allowing algorithms to run in
quasi-polynomial time.Comment: The full version of FOCS 201
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads via
platooning, even when human drivers and autonomous vehicles share roads.
However, when users of a road network choose their routes selfishly, the
resulting traffic configuration may be very inefficient. Because of this, we
consider how to influence human decisions so as to decrease congestion on these
roads. We consider a network of parallel roads with two modes of
transportation: (i) human drivers who will choose the quickest route available
to them, and (ii) ride hailing service which provides an array of autonomous
vehicle ride options, each with different prices, to users. In this work, we
seek to design these prices so that when autonomous service users choose from
these options and human drivers selfishly choose their resulting routes, road
usage is maximized and transit delay is minimized. To do so, we formalize a
model of how autonomous service users make choices between routes with
different price/delay values. Developing a preference-based algorithm to learn
the preferences of the users, and using a vehicle flow model related to the
Fundamental Diagram of Traffic, we formulate a planning optimization to
maximize a social objective and demonstrate the benefit of the proposed routing
and learning scheme.Comment: Submitted to CDC 201
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