26,735 research outputs found
A System for Distributed Mechanisms: Design, Implementation and Applications
We describe here a structured system for distributed mechanism design
appropriate for both Intranet and Internet applications. In our approach the
players dynamically form a network in which they know neither their neighbours
nor the size of the network and interact to jointly take decisions. The only
assumption concerning the underlying communication layer is that for each pair
of processes there is a path of neighbours connecting them. This allows us to
deal with arbitrary network topologies.
We also discuss the implementation of this system which consists of a
sequence of layers. The lower layers deal with the operations that implement
the basic primitives of distributed computing, namely low level communication
and distributed termination, while the upper layers use these primitives to
implement high level communication among players, including broadcasting and
multicasting, and distributed decision making.
This yields a highly flexible distributed system whose specific applications
are realized as instances of its top layer. This design is implemented in Java.
The system supports at various levels fault-tolerance and includes a
provision for distributed policing the purpose of which is to exclude
`dishonest' players. Also, it can be used for repeated creation of dynamically
formed networks of players interested in a joint decision making implemented by
means of a tax-based mechanism. We illustrate its flexibility by discussing a
number of implemented examples.Comment: 36 pages; revised and expanded versio
False-name-proof combinatorial auction design via single-minded decomposition
This paper proposes a new approach to building false-name-proof (FNP) combinatorial auctions from those that are FNP only with single-minded bidders, each of whom requires only one particular bundle. Under this approach, a general bidder is decomposed into a set of single-minded bidders, and after the decomposition the price and the allocation are determined by the FNP auctions for single-minded bidders. We first show that the auctions we get with the single-minded decomposition are FNP if those for single-minded bidders satisfy a condition called PIA. We then show that another condition, weaker than PIA, is necessary for the decomposition to build FNP auctions. To close the gap between the two conditions, we have found another sufficient condition weaker than PIA for the decomposition to produce strategy-proof mechanisms. Furthermore, we demonstrate that once we have PIA, the mechanisms created by the decomposition actually satisfy a stronger version of false-name-proofness, called false-name-proofness with withdrawal
Redesigning Bitcoin's fee market
The security of the Bitcoin system is based on having a large amount of
computational power in the hands of honest miners. Such miners are incentivized
to join the system and validate transactions by the payments issued by the
protocol to anyone who creates blocks. As new bitcoins creation rate decreases
(halving every 4 years), the revenue derived from transaction fees start to
have an increasingly important role. We argue that Bitcoin's current fee market
does not extract revenue well when blocks are not congested. This effect has
implications for the scalability debate: revenue from transaction fees may
decrease if block size is increased.
The current mechanism is a "pay your bid" auction in which included
transactions pay the amount they suggested. We propose two alternative auction
mechanisms: The Monopolistic Price Mechanism, and the Random Sampling Optimal
Price Mechanism (due to Goldberg et al.). In the monopolistic price mechanism,
the miner chooses the number of accepted transactions in the block, and all
transactions pay exactly the smallest bid included in the block. The mechanism
thus sets the block size dynamically (up to a bound required for fast block
propagation and other security concerns). We show, using analysis and
simulations, that this mechanism extracts revenue better from users, and that
it is nearly incentive compatible: the profit due to strategic bidding relative
to honest biding decreases as the number of bidders grows. Users can then
simply set their bids truthfully to exactly the amount they are willing to pay
to transact, and do not need to utilize fee estimate mechanisms, do not resort
to bid shading and do not need to adjust transaction fees (via replace-by-fee
mechanisms) if the mempool grows.
We discuss these and other properties of our mechanisms, and explore various
desired properties of fee market mechanisms for crypto-currencies
IIFA: Modular Inter-app Intent Information Flow Analysis of Android Applications
Android apps cooperate through message passing via intents. However, when
apps do not have identical sets of privileges inter-app communication (IAC) can
accidentally or maliciously be misused, e.g., to leak sensitive information
contrary to users expectations. Recent research considered static program
analysis to detect dangerous data leaks due to inter-component communication
(ICC) or IAC, but suffers from shortcomings with respect to precision,
soundness, and scalability. To solve these issues we propose a novel approach
for static ICC/IAC analysis. We perform a fixed-point iteration of ICC/IAC
summary information to precisely resolve intent communication with more than
two apps involved. We integrate these results with information flows generated
by a baseline (i.e. not considering intents) information flow analysis, and
resolve if sensitive data is flowing (transitively) through components/apps in
order to be ultimately leaked. Our main contribution is the first fully
automatic sound and precise ICC/IAC information flow analysis that is scalable
for realistic apps due to modularity, avoiding combinatorial explosion: Our
approach determines communicating apps using short summaries rather than
inlining intent calls, which often requires simultaneously analyzing all tuples
of apps. We evaluated our tool IIFA in terms of scalability, precision, and
recall. Using benchmarks we establish that precision and recall of our
algorithm are considerably better than prominent state-of-the-art analyses for
IAC. But foremost, applied to the 90 most popular applications from the Google
Playstore, IIFA demonstrated its scalability to a large corpus of real-world
apps. IIFA reports 62 problematic ICC-/IAC-related information flows via two or
more apps/components
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