No Arabic abstract
We analyze the value to e-commerce website operators of offering privacy options to users, e.g., of allowing users to opt out of ad targeting. In particular, we assume that site operators have some control over the cost that a privacy option imposes on users and ask when it is to their advantage to make such costs low. We consider both the case of a single site and the case of multiple sites that compete both for users who value privacy highly and for users who value it less. One of our main results in the case of a single site is that, under normally distributed utilities, if a privacy-sensitive user is worth at least $sqrt{2} - 1$ times as much to advertisers as a privacy-insensitive user, the site operator should strive to make the cost of a privacy option as low as possible. In the case of multiple sites, we show how a Prisoners-Dilemma situation can arise: In the equilibrium in which both sites are obliged to offer a privacy option at minimal cost, both sites obtain lower revenue than they would if they colluded and neither offered a privacy option.
Recent work has demonstrated that by monitoring the Real Time Bidding (RTB) protocol, one can estimate the monetary worth of different users for the programmatic advertising ecosystem, even when the so-called winning bids are encrypted. In this paper we describe how to implement the above techniques in a practical and privacy preserving manner. Specifically, we study the privacy consequences of reporting back to a centralized server, features that are necessary for estimating the value of encrypted winning bids. We show that by appropriately modulating the granularity of the necessary information and by scrambling the communication channel to the server, one can increase the privacy performance of the system in terms of K-anonymity. Weve implemented the above ideas on a browser extension and disseminated it to some 200 users. Analyzing the results from 6 months of deployment, we show that the average value of users for the programmatic advertising ecosystem has grown more than 75% in the last 3 years.
We study a problem of privacy-preserving mechanism design. A data collector wants to obtain data from individuals to perform some computations. To relieve the privacy threat to the contributors, the data collector adopts a privacy-preserving mechanism by adding random noise to the computation result, at the cost of reduced accuracy. Individuals decide whether to contribute data when faced with the privacy issue. Due to the intrinsic uncertainty in privacy protection, we model individuals privacy-related decision using Prospect Theory. Such a theory more accurately models individuals behavior under uncertainty than the traditional expected utility theory, whose prediction always deviates from practical human behavior. We show that the data collectors utility maximization problem involves a polynomial of high and fractional order, the root of which is difficult to compute analytically. We get around this issue by considering a large population approximation, and obtain a closed-form solution that well approximates the precise solution. We discover that the data collector who considers the more realistic Prospect Theory based individual decision modeling would adopt a more conservative privacy-preserving mechanism, compared with the case based on the expected utility theory modeling. We also study the impact of Prospect Theory parameters, and concludes that more loss-averse or risk-seeking individuals will trigger a more conservative mechanism. When individuals have different Prospect Theory parameters, simulations demonstrate that the privacy protection first becomes stronger and then becomes weaker as the heterogeneity increases from a low value to a high one.
Modern ad auctions allow advertisers to target more specific segments of the user population. Unfortunately, this is not always in the best interest of the ad platform. In this paper, we examine the following basic question in the context of second-price ad auctions: how should an ad platform optimally reveal information about the ad opportunity to the advertisers in order to maximize revenue? We consider a model in which bidders valuations depend on a random state of the ad opportunity. Different from previous work, we focus on a more practical, and challenging, situation where the space of possible realizations of ad opportunities is extremely large. We thus focus on developing algorithms whose running time is independent of the number of ad opportunity realizations. We examine the auctioneers algorithmic question of designing the optimal signaling scheme. When the auctioneer is restricted to send a public signal to all bidders, we focus on a well-motivated Bayesian valuation setting in which the auctioneer and bidders both have private information, and present two main results: 1. we exhibit a characterization result regarding approximately optimal schemes and prove that any constant-approximate public signaling scheme must use exponentially many signals; 2. we present a simple public signaling scheme that serves as a constant approximation under mild assumptions. We then initiate an exploration on the power of being able to send different signals privately to different bidders. Here we examine a basic setting where the auctioneer knows bidders valuations, and exhibit a polynomial-time private scheme that extracts almost full surplus even in the worst Bayes Nash equilibrium. This illustrates the surprising power of private signaling schemes in extracting revenue.
This paper studies equilibrium quality of semi-separable position auctions (known as the Ad Types setting) with greedy or optimal allocation combined with generalized second-price (GSP) or Vickrey-Clarke-Groves (VCG) pricing. We make three contributions: first, we give upper and lower bounds on the Price of Anarchy (PoA) for auctions which use greedy allocation with GSP pricing, greedy allocations with VCG pricing, and optimal allocation with GSP pricing. Second, we give Bayes-Nash equilibrium characterizations for two-player, two-slot instances (for all auction formats) and show that there exists both a revenue hierarchy and revenue equivalence across some formats. Finally, we use no-regret learning algorithms and bidding data from a large online advertising platform and no-regret learning algorithms to evaluate the performance of the mechanisms under semi-realistic conditions. For welfare, we find that the optimal-to-realized welfare ratio (an empirical PoA analogue) is broadly better than our upper bounds on PoA; For revenue, we find that the hierarchy in practice may sometimes agree with simple theory, but generally appears sensitive to the underlying distribution of bidder valuations.
We study the necessity of interaction between individuals for obtaining approximately efficient allocations. The role of interaction in markets has received significant attention in economic thinking, e.g. in Hayeks 1945 classic paper. We consider this problem in the framework of simultaneous communication complexity. We analyze the amount of simultaneous communication required for achieving an approximately efficient allocation. In particular, we consider two settings: combinatorial auctions with unit demand bidders (bipartite matching) and combinatorial auctions with subadditive bidders. For both settings we first show that non-interactive systems have enormous communication costs relative to interactive ones. On the other hand, we show that limited interaction enables us to find approximately efficient allocations.