Online advertising: analysis of privacy threats and protection approaches

Online advertising, the pillar of the “free” content on the Web, has revolutionized the marketing business in recent years by creating a myriad of new opportunities for advertisers to reach potential customers. The current advertising model builds upon an intricate infrastructure composed of a variety of intermediary entities and technologies whose main aim is to deliver personalized ads. For this purpose, a wealth of user data is collected, aggregated, processed and traded behind the scenes at an unprecedented rate. Despite the enormous value of online advertising, however, the intrusiveness and ubiquity of these practices prompt serious privacy concerns. This article surveys the online advertising infrastructure and its supporting technologies, and presents a thorough overview of the underlying privacy risks and the solutions that may mitigate them. We first analyze the threats and potential privacy attackers in this scenario of online advertising. In particular, we examine the main components of the advertising infrastructure in terms of tracking capabilities, data collection, aggregation level and privacy risk, and overview the tracking and data-sharing technologies employed by these components. Then, we conduct a comprehensive survey of the most relevant privacy mechanisms, and classify and compare them on the basis of their privacy guarantees and impact on the Web.Peer ReviewedPostprint (author's final draft

Unique bid auctions: Equilibrium solutions and experimental evidence

Two types of auction were introduced on the Internet a few years ago and have rapidly been gaining widespread popularity. In both auctions, players compete for an exogenously determined prize by independently choosing an integer in some finite and common strategy space specified by the auctioneer. In the unique lowest (highest) bid auction, the winner of the prize is the player who submits the lowest (highest) bid, provided that it is unique. We construct the symmetric mixed-strategy equilibrium solutions to the two auctions, and then test them in a sequence of experiments that vary the number of bidders and size of the strategy space. Our results show that the aggregate bids, but only a minority of the individual bidders, are accounted for quite accurately by the equilibrium solutions.

An Incentive Compatible Multi-Armed-Bandit Crowdsourcing Mechanism with Quality Assurance

Consider a requester who wishes to crowdsource a series of identical binary labeling tasks to a pool of workers so as to achieve an assured accuracy for each task, in a cost optimal way. The workers are heterogeneous with unknown but fixed qualities and their costs are private. The problem is to select for each task an optimal subset of workers so that the outcome obtained from the selected workers guarantees a target accuracy level. The problem is a challenging one even in a non strategic setting since the accuracy of aggregated label depends on unknown qualities. We develop a novel multi-armed bandit (MAB) mechanism for solving this problem. First, we propose a framework, Assured Accuracy Bandit (AAB), which leads to an MAB algorithm, Constrained Confidence Bound for a Non Strategic setting (CCB-NS). We derive an upper bound on the number of time steps the algorithm chooses a sub-optimal set that depends on the target accuracy level and true qualities. A more challenging situation arises when the requester not only has to learn the qualities of the workers but also elicit their true costs. We modify the CCB-NS algorithm to obtain an adaptive exploration separated algorithm which we call { \em Constrained Confidence Bound for a Strategic setting (CCB-S)}. CCB-S algorithm produces an ex-post monotone allocation rule and thus can be transformed into an ex-post incentive compatible and ex-post individually rational mechanism that learns the qualities of the workers and guarantees a given target accuracy level in a cost optimal way. We provide a lower bound on the number of times any algorithm should select a sub-optimal set and we see that the lower bound matches our upper bound upto a constant factor. We provide insights on the practical implementation of this framework through an illustrative example and we show the efficacy of our algorithms through simulations

E-Government: An e-reverse auction case study

This paper presents the findings from an exploratory in-depth case study with regard to e- Reverse Auctions (eRAs) in the UK public sector. These findings highlight that eRAs can improve procurement processes, realise cost savings and reduce delivery times. The research also notes that eRAs can produce dis-benefits, including poor buyer and supplier relationships and social implications. The paper elicits lessons learnt from the case study analysis. These are presented with the aim to inform theory and practice

Unique bid auctions: Equilibrium solutions and experimental evidence

Two types of auction were introduced on the Internet a few years ago and have rapidly been gaining widespread popularity. In both auctions, players compete for an exogenously determined prize by independently choosing an integer in some finite and common strategy space specified by the auctioneer. In the unique lowest (highest) bid auction, the winner of the prize is the player who submits the lowest (highest) bid, provided that it is unique. We construct the symmetric mixed-strategy equilibrium solutions to the two auctions, and then test them in a sequence of experiments that vary the number of bidders and size of the strategy space. Our results show that the aggregate bids, but only a minority of the individual bidders, are accounted for quite accurately by the equilibrium solutions.unique bid auctions; equilibrium analysis; experiment

Anglo-Dutch premium auctions in eighteenth-century Amsterdam

An Anglo-Dutch premium auction consists of an English auction followed by a Dutch auction, with a cash premium paid to the winner of the first round. We study such auctions used in the secondary debt market in eighteenth-century Amsterdam. This was among the first uses of auctions, or any structured market-clearing mechanism, in a financial market. We find that this market presented two distinct challenges - generating competition and aggregating information. We argue that the Anglo-Dutch premium auction is particularly well-suited to do both. Modeling equilibrium play theoretically, we predict a positive relationship between the uncertainty in a security's value and the likelihood of a second-round bid. Analyzing data on 16,854 securities sold in the late 1700s, we find empirical support for this prediction. This suggests that bidding behavior may have been consistent with (non-cooperative) equilibrium play, and therefore that these auctions were successful at generating competition. We also find evidence suggesting that these auctions succeeded at aggregating information. Thus, the Anglo-Dutch premium auction appears to have been an effective solution to a complex early market design problem

Electronic Reverse Auctions: Spawning Procurement Innovation in the Context of Arab Culture

Government e-procurement initiatives have the potential to transform local institutions, but few studies have been published of strategies for implementing specific e-procurement tools, particularly involving procurement by a foreign government adapting to local culture in the Middle East/North Africa (MENA). This case describes procurement at a forward operating base (FOB) in Kuwait in support of operations in Iraq. The government procurers had to deal with a phenomenon unique to the MENA region: wasta. Wasta is a form of social capital that bestows power, influence, and connection to those who possess it, similar to guanxi in China. This study explores the value proposition and limitations of electronic reverse auctions (eRA) with the purpose of sharing best practices and lessons learned for government procurement in a MENA country. The public value framework provides valuable theoretical insights for the implementation of a new government e-procurement tool in a foreign country. In a culture dominated by wasta, the suppliers enjoyed the transparency and merit-based virtues of eRA’s that transferred successfully into the new cultural milieu: potential to increase transparency, competition, efficiency, and taxpayer savings. The practices provided herein are designed specifically to help buyers overcome structural barriers including training, organizational inertia, and a lack of eRA policy and guidance while implementing a new e-procurement tool in a foreign country

The impact of the irrelevant – Temporary buy-options and bidding behavior in online auctions

In a laboratory experiment, we investigate the impact of temporary buy-options on efficiency, revenues, and bidding behavior in online proxy-auctions when bidders have independent private valuations. We show that the introduction of a buy-option reduces efficiency and at the same time fails to enhance revenues. In particular, we observe that the former presence of a temporary buy-option lowers final prices in an auction (even though the option is no longer available once an auction has started). If bidders have imprecise information about their private value, auction prices are increasing in the price of the buy-option which suggests anchoring as an explanation. Surprisingly, the former presence of a temporary buy-option also tends to reduce final auction prices if bidders are perfectly informed about their private value. In fact, we demonstrate that bidders are reluctant to bid above the option price regardless of the precision of their private information and the price of the option.microeconomics ;

A Game-theoretic Machine Learning Approach for Revenue Maximization in Sponsored Search

Sponsored search is an important monetization channel for search engines, in which an auction mechanism is used to select the ads shown to users and determine the prices charged from advertisers. There have been several pieces of work in the literature that investigate how to design an auction mechanism in order to optimize the revenue of the search engine. However, due to some unrealistic assumptions used, the practical values of these studies are not very clear. In this paper, we propose a novel \emph{game-theoretic machine learning} approach, which naturally combines machine learning and game theory, and learns the auction mechanism using a bilevel optimization framework. In particular, we first learn a Markov model from historical data to describe how advertisers change their bids in response to an auction mechanism, and then for any given auction mechanism, we use the learnt model to predict its corresponding future bid sequences. Next we learn the auction mechanism through empirical revenue maximization on the predicted bid sequences. We show that the empirical revenue will converge when the prediction period approaches infinity, and a Genetic Programming algorithm can effectively optimize this empirical revenue. Our experiments indicate that the proposed approach is able to produce a much more effective auction mechanism than several baselines.Comment: Twenty-third International Conference on Artificial Intelligence (IJCAI 2013