5,463 research outputs found
Mechanisms for Risk Averse Agents, Without Loss
Auctions in which agents' payoffs are random variables have received
increased attention in recent years. In particular, recent work in algorithmic
mechanism design has produced mechanisms employing internal randomization,
partly in response to limitations on deterministic mechanisms imposed by
computational complexity. For many of these mechanisms, which are often
referred to as truthful-in-expectation, incentive compatibility is contingent
on the assumption that agents are risk-neutral. These mechanisms have been
criticized on the grounds that this assumption is too strong, because "real"
agents are typically risk averse, and moreover their precise attitude towards
risk is typically unknown a-priori. In response, researchers in algorithmic
mechanism design have sought the design of universally-truthful mechanisms ---
mechanisms for which incentive-compatibility makes no assumptions regarding
agents' attitudes towards risk.
We show that any truthful-in-expectation mechanism can be generically
transformed into a mechanism that is incentive compatible even when agents are
risk averse, without modifying the mechanism's allocation rule. The transformed
mechanism does not require reporting of agents' risk profiles. Equivalently,
our result can be stated as follows: Every (randomized) allocation rule that is
implementable in dominant strategies when players are risk neutral is also
implementable when players are endowed with an arbitrary and unknown concave
utility function for money.Comment: Presented at the workshop on risk aversion in algorithmic game theory
and mechanism design, held in conjunction with EC 201
Secure and Efficient RNS Approach for Elliptic Curve Cryptography
Scalar multiplication, the main operation in elliptic
curve cryptographic protocols, is vulnerable to side-channel
(SCA) and fault injection (FA) attacks. An efficient countermeasure
for scalar multiplication can be provided by using alternative
number systems like the Residue Number System (RNS). In RNS,
a number is represented as a set of smaller numbers, where each
one is the result of the modular reduction with a given moduli
basis. Under certain requirements, a number can be uniquely
transformed from the integers to the RNS domain (and vice
versa) and all arithmetic operations can be performed in RNS.
This representation provides an inherent SCA and FA resistance
to many attacks and can be further enhanced by RNS arithmetic
manipulation or more traditional algorithmic countermeasures.
In this paper, extending our previous work, we explore the
potentials of RNS as an SCA and FA countermeasure and provide
an description of RNS based SCA and FA resistance means. We
propose a secure and efficient Montgomery Power Ladder based
scalar multiplication algorithm on RNS and discuss its SCAFA
resistance. The proposed algorithm is implemented on an
ARM Cortex A7 processor and its SCA-FA resistance is evaluated
by collecting preliminary leakage trace results that validate our
initial assumptions
A deterministic truthful PTAS for scheduling related machines
Scheduling on related machines () is one of the most important
problems in the field of Algorithmic Mechanism Design. Each machine is
controlled by a selfish agent and her valuation can be expressed via a single
parameter, her {\em speed}. In contrast to other similar problems, Archer and
Tardos \cite{AT01} showed that an algorithm that minimizes the makespan can be
truthfully implemented, although in exponential time. On the other hand, if we
leave out the game-theoretic issues, the complexity of the problem has been
completely settled -- the problem is strongly NP-hard, while there exists a
PTAS \cite{HS88,ES04}.
This problem is the most well studied in single-parameter algorithmic
mechanism design. It gives an excellent ground to explore the boundary between
truthfulness and efficient computation. Since the work of Archer and Tardos,
quite a lot of deterministic and randomized mechanisms have been suggested.
Recently, a breakthrough result \cite{DDDR08} showed that a randomized truthful
PTAS exists. On the other hand, for the deterministic case, the best known
approximation factor is 2.8 \cite{Kov05,Kov07}.
It has been a major open question whether there exists a deterministic
truthful PTAS, or whether truthfulness has an essential, negative impact on the
computational complexity of the problem. In this paper we give a definitive
answer to this important question by providing a truthful {\em deterministic}
PTAS
Artificial Intelligence and Statistics
Artificial intelligence (AI) is intrinsically data-driven. It calls for the
application of statistical concepts through human-machine collaboration during
generation of data, development of algorithms, and evaluation of results. This
paper discusses how such human-machine collaboration can be approached through
the statistical concepts of population, question of interest,
representativeness of training data, and scrutiny of results (PQRS). The PQRS
workflow provides a conceptual framework for integrating statistical ideas with
human input into AI products and research. These ideas include experimental
design principles of randomization and local control as well as the principle
of stability to gain reproducibility and interpretability of algorithms and
data results. We discuss the use of these principles in the contexts of
self-driving cars, automated medical diagnoses, and examples from the authors'
collaborative research
The STRESS Method for Boundary-point Performance Analysis of End-to-end Multicast Timer-Suppression Mechanisms
Evaluation of Internet protocols usually uses random scenarios or scenarios
based on designers' intuition. Such approach may be useful for average-case
analysis but does not cover boundary-point (worst or best-case) scenarios. To
synthesize boundary-point scenarios a more systematic approach is needed.In
this paper, we present a method for automatic synthesis of worst and best case
scenarios for protocol boundary-point evaluation.
Our method uses a fault-oriented test generation (FOTG) algorithm for
searching the protocol and system state space to synthesize these scenarios.
The algorithm is based on a global finite state machine (FSM) model. We extend
the algorithm with timing semantics to handle end-to-end delays and address
performance criteria. We introduce the notion of a virtual LAN to represent
delays of the underlying multicast distribution tree. The algorithms used in
our method utilize implicit backward search using branch and bound techniques
and start from given target events. This aims to reduce the search complexity
drastically. As a case study, we use our method to evaluate variants of the
timer suppression mechanism, used in various multicast protocols, with respect
to two performance criteria: overhead of response messages and response time.
Simulation results for reliable multicast protocols show that our method
provides a scalable way for synthesizing worst-case scenarios automatically.
Results obtained using stress scenarios differ dramatically from those obtained
through average-case analyses. We hope for our method to serve as a model for
applying systematic scenario generation to other multicast protocols.Comment: 24 pages, 10 figures, IEEE/ACM Transactions on Networking (ToN) [To
appear
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