Optimising Trade-offs Among Stakeholders in Ad Auctions

We examine trade-offs among stakeholders in ad auctions. Our metrics are the revenue for the utility of the auctioneer, the number of clicks for the utility of the users and the welfare for the utility of the advertisers. We show how to optimize linear combinations of the stakeholder utilities, showing that these can be tackled through a GSP auction with a per-click reserve price. We then examine constrained optimization of stakeholder utilities. We use simulations and analysis of real-world sponsored search auction data to demonstrate the feasible trade-offs, examining the effect of changing the allowed number of ads on the utilities of the stakeholders. We investigate both short term effects, when the players do not have the time to modify their behavior, and long term equilibrium conditions. Finally, we examine a combinatorially richer constrained optimization problem, where there are several possible allowed configurations (templates) of ad formats. This model captures richer ad formats, which allow using the available screen real estate in various ways. We show that two natural generalizations of the GSP auction rules to this domain are poorly behaved, resulting in not having a symmetric Nash equilibrium or having one with poor welfare. We also provide positive results for restricted cases.Comment: 18 pages, 10 figures, ACM Conference on Economics and Computation 201

Dwelling on the Negative: Incentivizing Effort in Peer Prediction

Agents are asked to rank two objects in a setting where effort is costly and agents differ in quality (which is the probability that they can identify the correct, ground truth, ranking). We study simple output-agreement mechanisms that pay an agent in the case she agrees with the report of another, and potentially penalizes for disagreement through a negative payment. Assuming access to a quality oracle, able to determine whether an agent's quality is above a given threshold, we design a payment scheme that aligns incentives so that agents whose quality is above this threshold participate and invest effort. Precluding negative payments leads the expected cost of this quality-oracle mechanism to increase by a factor of 2 to 5 relative to allowing both positive and negative payments. Dropping the assumption about access to a quality oracle, we further show that negative payments can be used to make agents with quality lower than the quality threshold choose to not to participate, while those above continue to participate and invest effort. Through the appropriate choice of payments, any design threshold can be achieved. This self-selection mechanism has the same expected cost as the cost-minimal quality-oracle mechanism, and thus when using the self-selection mechanism, perfect screening comes for free.Engineering and Applied Science

DUAS FACES DO PODER

Este artigo apresenta duas concepções de poder, a partir do exame e da crítica de duas tradições depesquisa. A tradição sociológica, que originou a corrente elitista, postula a existência do poder nascomunidades; a tradição politológica, que originou a corrente pluralista, questiona a existência de elitesdirigentes em comunidades e instituições. O artigo argumenta que a tradição elitista postula o que deveser provado, ao passo que a pluralista está correta em investigar se há de fato grupos governantes nassociedades, mas sua abordagem é restrita e deixa de lado um aspecto essencial da questão. Assim, osautores do artigo argumentam que, anteriormente à face visível do poder, manifestada pelos indivíduos egrupos que tomam efetivamente as decisões (ou que impõem os vetos), os pesquisadores devem prestaratenção à face invisível do poder. Essa outra face consiste na capacidade que indivíduos ou grupos têm decontrolar ou manipular os valores sociais e políticos (isto é, de “mobilizar vieses”), impedindo que temaspotencialmente perigosos para seus interesses e perspectivas sejam objeto de discussão e deliberaçãopública

The shared assignment game and applications to pricing in cloud computing

ABSTRACT We propose an extension to the Assignment Gam

Secure Data Exchange: A Marketplace in the Cloud

A vast amount of data belonging to companies and individuals is currently stored \emph{in the cloud} in encrypted form by trustworthy service providers such as Microsoft, Amazon, and Google. Unfortunately, the only way for the cloud to use the data in computations is to first decrypt it, then compute on it, and finally re-encrypt it, resulting in a problematic trade-off between value/utility and security. At a high level, our goal in this paper is to present a general and practical cryptographic solution to this dilemma. More precisely, we describe a scenario that we call \emph{Secure Data Exchange} (SDE), where several data owners are storing private encrypted data in a semi-honest non-colluding cloud, and an evaluator (a third party) wishes to engage in a secure function evaluation on the data belonging to some subset of the data owners. We require that none of the parties involved learns anything beyond what they already know and what is revealed by the function, even when the parties (except the cloud) are active malicious. We also recognize the ubiquity of scenarios where the lack of an efficient SDE protocol prevents for example business transactions, research collaborations, or mutually beneficial computations on aggregated private data from taking place, and discuss several such scenarios in detail. Our main result is an efficient and practical protocol for enabling SDE using \emph{Secure Multi-Party Computation}~(MPC) in a novel adaptation of the server-aided setting. We also present the details of an implementation along with performance numbers

Secure Data Exchange: A Marketplace in the Cloud

Abstract A vast amount of data belonging to companies and individuals is currently stored in the cloud in encrypted form by trustworthy service providers such as Microsoft, Amazon, and Google. Unfortunately, the only way for the cloud to use the data in computations is to first decrypt it, then compute on it, and finally re-encrypt it, resulting in a problematic trade-off between value/utility and security. At a high level, our goal in this paper is to present a general and practical cryptographic solution to this dilemma. More precisely, we describe a scenario that we call Secure Data Exchange (SDE), where several data owners are storing private encrypted data in a semi-honest non-colluding cloud, and an evaluator (a third party) wishes to engage in a secure function evaluation on the data belonging to some subset of the data owners. We require that none of the parties involved learns anything beyond what they already know and what is revealed by the function, even when the parties (except the cloud) are active malicious. We also recognize the ubiquity of scenarios where the lack of an efficient SDE protocol prevents for example business transactions, research collaborations, or mutually beneficial computations on aggregated private data from taking place, and discuss several such scenarios in detail. Our main result is an efficient and practical protocol for enabling SDE using Secure MultiParty Computation (MPC) in a novel adaptation of the server-aided setting. We also present the details of an implementation along with performance numbers

Private Collaborative Neural Network Learning

Machine learning algorithms, such as neural networks, create better predictive models when having access to larger datasets. In many domains, such as medicine and finance, each institute has only access to limited amounts of data, and creating larger datasets typically requires collaboration. However, there are privacy related constraints on these collaborations for legal, ethical, and competitive reasons. In this work, we present a feasible protocol for learning neural networks in a collaborative way while preserving the privacy of each record. This is achieved by combining Differential Privacy and Secure Multi-Party Computation with Machine Learning

Building A Personalized Tourist Attraction Recommender System Using Crowdsourcing (Demonstration)

ABSTRACT We demonstrate how crowdsourcing can be used to automatically build a personalized tourist attraction recommender system, which tailors recommendations to specific individuals, so different people who use the system each get their own list of recommendations, appropriate to their own traits. Recommender systems crucially depend on the availability of reliable and large scale data that allows predicting how a new individual is likely to rate items from the catalog of possible items to recommend. We show how to automate the process of generating this data using crowdsourcing, so that such a system can be built even when such a dataset is not initially available. We first find possible tourist attractions to recommend by scraping such information from Wikipedia. Next, we use crowdsourced workers to filter the data, then provide their opinions regarding these items. Finally, we use machine learning methods to predict how new individuals are likely to rate each attraction, and recommend the items with the highest predicted ratings