Abstract
Online advertising has motivated companies to collect vast amounts of information about users, which increasingly creates privacy concerns. One way to answer these concerns is by enabling end users to choose which aspects of their private information can be collected. Based on principles suggested by Feldman and Gonen (2018), we introduce a new online advertising market model which uses information brokers to give users such control. Unlike Feldman and Gonen (2018), our model is dynamic and involves multi-sided markets where all participating sides are strategic. We describe a mechanism for this model which is theoretically guaranteed to (approximately) maximize the gain from trade, avoid a budget deficit and incentivize truthfulness and voluntary participation. As far as we know, this is the first known dynamic mechanism for a multi-sided market having these properties.
We experimentally examine and compare our theoretical results using real world advertising bid data. The experiments suggest that our mechanism performs well in practice even in regimes for which our theoretical guarantee is weak or irrelevant.
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Notes
- 1.
The use of mediators is necessary because it should not be possible to link an information portfolio offered for sell on the market to any particular user, which prevents users from interacting directly with the market.
- 2.
Blum et al. [4] has multiple different objectives (maximizing profit, liquidity and welfare). We refer to the objective which is most relevant to our work.
- 3.
Informally, an agent is truthful if he/she reports the information as it is known to him/her. A formal definition of what it means for a user, mediator or advertiser to be truthful is given in Sect. 2.
- 4.
Here and throughout the paper, a reference to domination of strategies should always be understood as a reference to weak domination.
- 5.
A mechanism is budget balanced if the amount it charges (from the advertisers) is at least as large as the amount it pays.
- 6.
The mediators’ utility functions are independent of the amount of money transferred from the mediators to the users. This choice was made with the aim of balancing two of the mediators’ conflicting objectives: on the one hand, mediators want to make as much money as possible, and on the other hand, they want to acquire users and have them use their services rather than switch to another mediator who is known for paying more money to his users.
- 7.
In some cases the assumption that the mechanism has a prior knowledge about the number of entities might be considered unnatural. The mechanism we present can be modified using standard techniques to work with an alternative assumption stating that each entity arrives at a uniformly random time from some range (for example, [0, 1]). See [11] for more details.
- 8.
Our experiments are based on only a fraction of the entire data set, which significantly increased the market strength of the entities in the input. To compensate for this increase, and keep the market strength of each advertiser in the simulation similar to the market strength of the corresponding real world advertiser, we introduced a division by 100 into the capacity formula.
- 9.
Note that the experiments did not simulate the information trading part of the model since they were intended to study the mechanism’s competitive ratio. However, information exchange can occur in our model in practice. Intuitively, one can think of the users in a single execution of the mechanism as the users who agreed to sell information that, if revealed, implies that they have one particular type t. Then, if an advertiser’s ad is shown to a user, the advertiser may learn that the user is of type t and the user is monetarily compensated for that.
References
Ashlagi, I., Monderer, D., Tennenholtz, M.: Mediators in position auctions. Games Econ. Behav. 67, 2–21 (2009)
Babaioff, M., Dinitz, M., Gupta, A., Immorlica, N., Talwar, K.: Secretary problems: weights and discounts. In: SODA, pp. 1245–1254 (2009)
Babaioff, M., Feldman, M., Tennenholtz, M.: Mechanism design with strategic mediators. ACM Trans. Econ. Comput. 4, 7:1–7:48 (2016)
Blum, A., Sandholm, T., Zinkevich, M.: Online algorithms for market clearing. In: SODA, pp. 971–980 (2002)
Bredin, J., Parkes, D., Duong, Q.: Chain: a dynamic double auction framework for matching patient agents. J. Artif. Intell. Res. 30, 133–179 (2007)
Charikar, M., Henzinger, M., Nguyen, H.L.: Online bipartite matching with decomposable weights. In: Schulz, A.S., Wagner, D. (eds.) ESA 2014. LNCS, vol. 8737, pp. 260–271. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44777-2_22
Conitzer, V., Taylor, C.R., Wagman, L.: Hide and seek: costly consumer privacy in a market with repeat purchases. Mark. Sci. 31(2), 277–292 (2012)
Facebook: CEO Mark Zuckerberg Testifies Before Senate on User Data (2018). https://m.youtube.com/watch?v=qAZiDRonYZI
Feldman, M., Gonen, R.: Markets with strategic multi-minded mediators. CoRR abs/1603.08717 (2016). http://arxiv.org/abs/1603.08717
Feldman, M., Gonen, R.: Removal and threshold pricing: truthful two-sided markets with multi-dimensional participants. In: Deng, X. (ed.) SAGT 2018. LNCS, vol. 11059, pp. 163–175. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99660-8_15
Feldman, M., Naor, J.S., Schwartz, R.: Improved competitive ratios for submodular secretary problems (extended abstract). In: Goldberg, L.A., Jansen, K., Ravi, R., Rolim, J.D.P. (eds.) APPROX/RANDOM -2011. LNCS, vol. 6845, pp. 218–229. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22935-0_19
Feldman, M., Svensson, O., Zenklusen, R.: A simple \(O\)(log log (rank))-competitive algorithm for the matroid secretary problem. Math. Oper. Res. 43(2), 638–650 (2018)
Gonen, M., Gonen, R., Pavlov, E.: Generalized trade reduction mechanisms. In: EC, pp. 20–29. ACM, New York (2007)
Karp, R.M., Vazirani, U.V., Vazirani, V.V.: An optimal algorithm for on-line bipartite matching. In: STOC, pp. 352–358 (1990)
McAfee, R.P.: dominant strategy double auction. J. Econ. Theory 56, 434–450 (1992)
Myerson, R.B., Satterthwaite, M.A.: Efficient mechanisms for bilateral trading. J. Econ. Theory 29, 265–281 (1983)
Segal-Halevi, E., Hassidim, A., Aumann, Y.: MUDA: a truthful multi-unit double-auction mechanism. In: Proceeding of AAAI (2018)
Stavrogiannis, L.C., Gerding, E.H., Polukarov, M.: Auction mechanisms for demand-side intermediaries in online advertising exchanges. In: AAMAS, pp. 5–9. International Foundation for Autonomous Agents and Multiagent Systems, Richland (2014)
Vaze, R., Coupechoux, M.: Online budgeted truthful matching. SIGMETRICS Perform. Eval. Rev. 44(3), 3–6 (2016)
Wurman, P., Walsh, W., Wellman, M.: Flexible double auctions for electronic commerce: theory and implementation. Decis. Support Syst. 24, 17–27 (1998)
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Feldman, M., Frim, G., Gonen, R. (2018). Multi-sided Advertising Markets: Dynamic Mechanisms and Incremental User Compensations. In: Bushnell, L., Poovendran, R., Başar, T. (eds) Decision and Game Theory for Security. GameSec 2018. Lecture Notes in Computer Science(), vol 11199. Springer, Cham. https://doi.org/10.1007/978-3-030-01554-1_13
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