Abstract
In electronic anonymity markets a taker seeks a specified number of market makers in order to anonymize a transaction or activity. This process requires both coalition formation, in order to create an anonymity set among the taker and makers, and the derivation of the fee that the taker pays each maker. The process has a novel property in that the taker pays for anonymity but anonymity is created for both the taker and the makers. Using the Shapley value for nontransferable utility cooperative games, we characterize the formation of the anonymity set and the fee for any arbitrary number of makers selected by the taker.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The popular belief that Bitcoin payments are anonymous is wrong. This cryptocurrency uses pseudonymous accounts and a public transaction ledger. Agents who want to hide the relation between their accounts, some of which may fully identify them, need anonymizing technology [6].
- 2.
See http://joinmarket.io. Last visited on June 25th, 2018.
- 3.
See Meiklejohn and Orlandi [22] on the hardness of untangling CoinJoin transactions.
- 4.
The fee is composed of fixed and variable parts to account for contributions to the Bitcoin network’s miner fees. Our model abstracts from this complexity by assuming a normalized nominal transaction value.
- 5.
Public goods have the property that they are nonexclusive and nonrival [34]. Nonexclusive means that once created, the associated benefits of the good cannot be withheld from others. Technically, the nonexcludability property of anonymity applies only to the makers and taker engaged in the transaction. Nonrivalry means that use of the good does not prohibit its use by others.
- 6.
- 7.
In addition to the first-order risk of losing one’s identity, makers may also face the risk of legal authorities investigating Bitcoin purchases as part of a criminal investigation. This potentiality lies beyond the scope of the present paper.
- 8.
Technically, any \(y_i \le x_i\) is a potential payoff for player i as well. This property is known as “comprehensiveness” [28].
- 9.
Myerson [26, p. 16] offers an alternative interpretation: “With nontransferable utility, we have no grounds for interpersonal comparison of utility, so we may feel free to rescale either player’s utility separately by a positive scaling factor or utility weight \(\lambda _i\). Now, in the rescaled version of the game, pretend that the weighted-utility payoffs are transferable.”
- 10.
As the proof is based on a fixed point theorem it does not guarantee uniqueness. We are unaware of any example in the literature where multiple weights are derived that lead to alternative NTU Shapley values. If multiple fixed points exist, selecting among them is a well-defined problem. A natural criterion would be to maximize the taker’s payoff.
- 11.
This is consistent with finding a solution under the condition \(\lambda _t= \lambda _1 = \lambda _2\) (where all \(\lambda \)’s are finite), which yields an equivalent result.
- 12.
The term \(\delta D- F\) must be nonnegative; otherwise, the outside alternative is not viable for the taker.
References
Abramova, S., Schöttle, P., Böhme, R.: Mixing coins of different quality: a game-theoretic approach. In: Brenner, M., et al. (eds.) FC 2017. LNCS, vol. 10323, pp. 280–297. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-70278-0_18
Acquisti, A., Dingledine, R., Syverson, P.: On the economics of anonymity. In: Wright, R.N. (ed.) FC 2003. LNCS, vol. 2742, pp. 84–102. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-45126-6_7
Acquisti, A., Taylor, C., Wagman, L.: The economics of privacy. J. Econ. Lit. 54(2), 442–492 (2016)
Acquisti, A., Varian, H.R.: Conditioning prices on purchase history. Market. Sci. 24(3), 367–381 (2005)
Androulaki, E., Raykova, M., Srivatsan, S., Stavrou, A., Bellovin, S.M.: PAR: payment for anonymous routing. In: Borisov, N., Goldberg, I. (eds.) PETS 2008. LNCS, vol. 5134, pp. 219–236. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-70630-4_14
Böhme, R., Christin, N., Edelman, B., Moore, T.: Bitcoin: economics, technology, and governance. J. Econ. Perspect. 29(2), 213–238 (2015)
Bonneau, J., Narayanan, A., Miller, A., Clark, J., Kroll, J.A., Felten, E.W.: Mixcoin: anonymity for bitcoin with accountable mixes. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 486–504. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45472-5_31
Chessa, M., Grossklags, J., Loiseau, P.: A game-theoretic study on non-monetary incentives in data analytics projects with privacy implications. In: Fournet, C., Hicks, M.W., Viganò, L. (eds.) Proceedings of the Computer Security Foundations Symposium (CSF), pp. 90–104. IEEE Computer Society (2015)
Chessa, M., Loiseau, P.: A cooperative game-theoretic approach to quantify the value of personal data in networks. In: Proceedings of the 12th Workshop on the Economics of Networks, Systems and Computation, no. 9. ACM, Cambridge (2017)
Díaz, C., Seys, S., Claessens, J., Preneel, B.: Towards measuring anonymity. In: Dingledine, R., Syverson, P. (eds.) PET 2002. LNCS, vol. 2482, pp. 54–68. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36467-6_5
Dingledine, R., Mathewson, N., Syverson, P.: Tor: the second-generation onion router. In: 13th USENIX Security Symposium. USENIX Association (2004)
Dwork, C.: Differential privacy: a survey of results. In: Agrawal, M., Du, D., Duan, Z., Li, A. (eds.) TAMC 2008. LNCS, vol. 4978, pp. 1–19. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79228-4_1
Franz, E., Jerichow, A., Wicke, G.: A payment scheme for mixes providing anonymity. In: Lamersdorf, W., Merz, M. (eds.) TREC 1998. LNCS, vol. 1402, pp. 94–108. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0053404
Friedman, E., Resnick, P.: The social cost of cheap pseudonyms. J. Econ. Manag. Strategy 10(2), 173–199 (2001)
Gkatzelis, V., Aperjis, C., Huberman, B.A.: Pricing private data. Electron. Markets 25(2), 109–123 (2015)
Harsanyi, J.C.: A simplified bargaining model for the \(n\)-person cooperative game. Int. Econ. Rev. 4(2), 194–220 (1963)
Jansen, R., Johnson, A., Syverson, P.: LIRA: lightweight incentivized routing for anonymity. In: Proceedings of the Network and Distributed System Security Symposium (NDSS). The Internet Society (2013)
Kleinberg, J., Papadimitriou, C.H., Raghavan, P.: On the value of private information. In: Proceedings of the 8th Conference on Theoretical Aspects of Rationality and Knowledge (TARK), pp. 249–257. ACM, New York (2001)
Köpsell, S.: Low latency anonymous communication – how long are users willing to wait? In: Müller, G. (ed.) ETRICS 2006. LNCS, vol. 3995, pp. 221–237. Springer, Heidelberg (2006). https://doi.org/10.1007/11766155_16
Maschler, M., Owen, G.: The consistent Shapley value for games without sidepayments. In: Selten, R. (ed.) Rational Interaction, pp. 5–12. Springer, New York (1992). https://doi.org/10.1007/978-3-662-09664-2_2
Maxwell, G.: CoinJoin: Bitcoin privacy for the real world (2013). https://bitcointalk.org/index.php?topic=279249.0
Meiklejohn, S., Orlandi, C.: Privacy-enhancing overlays in bitcoin. In: Brenner, M., Christin, N., Johnson, B., Rohloff, K. (eds.) FC 2015. LNCS, vol. 8976, pp. 127–141. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48051-9_10
Möser, M., Böhme, R.: Join me on a market for anonymity. In: Workshop on the Economics of Information Security (WEIS), Berkeley, CA (2016)
Möser, M., Böhme, R.: The price of anonymity: empirical evidence from a market for Bitcoin anonymization. J. Cybersecur. 3(2), 127–135 (2017)
Möser, M., Böhme, R., Breuker, D.: An inquiry into money laundering tools in the Bitcoin ecosystem. In: Proceedings of the APWG E-Crime Researchers Summit, pp. 1–14. IEEE, San Francisco (2013)
Myerson, R.B.: Fictitious-transfer solutions in cooperative game theory. In: Selten, R. (ed.) Rational Interaction, pp. 13–33. Springer, Heidelberg (1992). https://doi.org/10.1007/978-3-662-09664-2_3
Narayanan, A., Bonneau, J., Felten, E., Miller, A., Goldfeder, S.: Bitcoin and Crypocurrency Technologies. A Comprehensive Introduction. Princeton University Press, Princeton (2016)
Owen, G.: Game Theory. W. B. Saunders, Philadelphia (1968)
Padilla, A.J., Pagano, M.: Sharing default information as a borrower discipline device. Eur. Econ. Rev. 44(10), 1951–1980 (2000)
Pagano, M., Jappelli, T.: Information sharing in credit markets. J. Finance 48(5), 1693–1718 (1993)
Peleg, B., Sudhölter, P.: Introduction to the Theory of Cooperative Games, 2nd edn. Kluwer Academic, Boston (2007)
Pfitzmann, A., Köhntopp, M.: Anonymity, unobservability, and pseudonymity - a proposal for terminology. In: Federrath, H. (ed.) Designing Privacy Enhancing Technologies. LNCS, vol. 2009, pp. 1–9. Springer, Heidelberg (2001)
Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Başar, T.: Coalitional game theory for communication networks: a tutorial (2009). https://arxiv.org/abs/0905.4057
Sandler, T.: Collective Action. University of Michigan Press, Ann Arbor (1992)
Serjantov, A., Danezis, G.: Towards an information theoretic metric for anonymity. In: Dingledine, R., Syverson, P. (eds.) PET 2002. LNCS, vol. 2482, pp. 41–53. Springer, Heidelberg (2003). https://doi.org/10.1007/3-540-36467-6_4
Shapley, L.S.: A value for \(n\)-person games. In: Kuhn, H.W., Tucker, A.W. (eds.) Contributions to the Theory of Games, vol. II, pp. 307–317. Princeton University Press, Princeton (1953)
Shapley, L.S.: Utility comparisons and the theory of games. In: La Décision: Aggrégation et Dynamique des Orders de Préférences, pp. 251–263. Éditions du Centre Nationale de la Recherche Scientifique, Paris (1969)
Shapley, L.S.: Utility comparisons and the theory of games. In: Roth, A.E. (ed.) The Shapley Value. Essays in Honor of Lloyd S. Shapley, pp. 307–319. Cambridge University Press (1988)
Sweeney, L.: \(k\)-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl.-Based Syst. 10(5), 571–588 (2002)
Zhu, H.: Do dark pools harm price discovery? Rev. Financ. Stud. 27(3), 747–780 (2014)
Acknowledgement
We thank the anonymous reviewers for helpful comments. The second author is funded in part by Archimedes Privatstiftung, Innsbruck, and the German Bundesministerium für Bildung und Forschung (BMBF) under grant agreement 16KIS0382.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendices
Appendix
A Shapley Value Derivation for the 3-Player Example
Following the formula given in Eq. (2), the Shapley value for the taker, \(t\), with the makers as players 1 and 2, is
Substituting in the worth function values, Eqs. 10–15,
(by convention, \(\omega (\emptyset )=0\)):
Aggregating terms,
and simplifying:
Using Eq. (2) to calculate the Shapley value for player 1, who is a maker:
Substituting in the worth function values:
Aggregating terms and simplifying:
By the symmetry property of the Shapley value, \(\varphi _2(\omega , \varvec{\lambda }) = \varphi _1(\omega , \varvec{\lambda })\). \(\square \)
B Proof of Theorem 2
The proof consists of three parts.
1.1 B.1 Shapley Value for the Taker
Proof
From Eq. (2), the coefficient on \(\omega (\{t\})-\omega (\emptyset ) = \delta D-F\) in the Shapley value is \(1/N= 1/(m+1)\). This is the first term in Eq. (28).
Given \(m\) makers, there are \({m\atopwithdelims ()n} = \frac{m!}{n!\,(m-n)!}\) combinations of coalitions that can be expressed as \(S=\{t, n\}\). Note also that \(N= m+1\). From Eq. (2), the coefficient on each coalition \(\{t, n\}\) in the Shapley value is
For each coalition, \(\{t, n\}\), the marginal contribution for the taker in the formula for \(\varphi _t\) is \(\bigl (\omega (\{t, n\})-\omega (\{n\})\bigr )=\omega (\{t, n\})\). In aggregate, the partial sum in the Shapley value for a specific \(n\) is the product of the following three terms: (i) the number of \(\{t, n\}\) coalitions, (ii) the Shapley coefficient that is common to each \(\{t, n\}\) coalition, (40), and (iii) \(\bigl (\omega (\{t, n\})-\omega (\{n\})\bigr )=\omega (\{t, n\})\), from Eq. (27):
Summing this over all possible \(n= 1, \dots , m\) yields the final two terms in Eq. (28). This completes the derivation of the Shapley value for the taker, \(\varphi _t/\lambda _t\), given that \(\lambda _t= 1\).
1.2 B.2 Shapley Value for the Makers
In deriving the Shapley value for maker i, \(\varphi _i\), note that for any coalition, \(\hat{S}\), where . This simplifies the remaining steps for calculating the Shapley value for i to only those worth functions whose coalitions include a taker, \(t\), as a member; i. e., \(\omega (\{t, n\})\).
Proof
For any maker, i, there is one and only one \(\{t, i\}\) coalition. The coefficient on this coalition in the Shapley value is
As \(\omega (\{t, i\})=\nicefrac {1}{2}D+ \nicefrac {1}{2}d\) and \(\omega (\{t, i\}\setminus \{i\}) = \delta D-F\), the part of the calculation of \(\varphi _i\) that corresponds to the marginal contribution of i to \(\{t, i\}\) is:
From Eq. (2) the calculation of the Shapley value is now:
The remainder of the coalitions where \(i \in \{t, n\}\) require \(n\ge 2\). For a given \(n\), the number of coalitions for which maker i is a member, \(i \in \{t, n\}\), is
Given \(n\), the coefficient in the Shapley value for the marginal contribution, \(\omega (\{t, n\})-\omega (\{t, n\}\setminus \{i\})\), of maker i is
To calculate \(\omega (\{t, n\})-\omega (\{t, n\}\setminus \{i\})\), use Eq. (27) and observe that
Consequently, i’s marginal contribution to the coalition \(\{t, n\}\) is
Hence, when \(n\ge 2\), for a given value of \(n\) the partial sum within the Shapley value corresponding to \(\{t, n\}\) such that \(i \in \{t, n\}\) is the product:
Summing this over all possible \(n= 2, \dots , m\) yields:
To derive \(\varphi _i\), combine this with the Shapley value term for \(\{t, i\}\), Eq. (43):
Aggregating terms:
This completes the derivation of the Shapley value for a maker, \(\varphi _i/\lambda _i\), given \(\lambda _i = 1\).
1.3 B.3 Feasibility Check
The final step requires verification that \(\varphi _i/\lambda _i\) is feasible for \(V(N)\). This is facilitated using the expressions of \(\varphi _t/\lambda _t\) and \(\varphi _i/\lambda _i\) in terms of harmonic numbers, as given in Eqs. (56) and (57) in Appendix C.
Proof
Recall that the fee, \(f\), given in Eq. (31) was derived from the feasibility condition for \(\varphi _t/\lambda _t\) when \(\lambda _t= 1\): \(\varphi _t\le \frac{m}{m+1}D-mf\), yielding \(f= \frac{1}{m+1}D-\frac{1}{m}\varphi _t\). The feasibility condition for \(\varphi _i/\lambda _i\) when \(\lambda _i=1\) is
Setting the two sides equal and substituting in the value for \(\varphi _t\), Eq. (56):
which reduces to
This is exactly the right side of Eq. (57). Hence the condition holds under the theorem.
C Alternative Form of Theorem 2 Using Harmonic Numbers
Let \(H_{m}\) denote the \(m\)-th harmonic number, i.e., \(H_{m}=\sum _{n=1}^m\frac{1}{n}\). Using this shorthand, Eq. (28) of Theorem 2 can be rewritten as,
and Eq. (29) becomes:
Rights and permissions
Copyright information
© 2018 International Financial Cryptography Association
About this paper
Cite this paper
Arce, D.G., Böhme, R. (2018). Pricing Anonymity. In: Meiklejohn, S., Sako, K. (eds) Financial Cryptography and Data Security. FC 2018. Lecture Notes in Computer Science(), vol 10957. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58387-6_19
Download citation
DOI: https://doi.org/10.1007/978-3-662-58387-6_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-58386-9
Online ISBN: 978-3-662-58387-6
eBook Packages: Computer ScienceComputer Science (R0)