The Price of Malice: A Game-Theoretic Framework for Malicious Behavior in Distributed Systems

In recent years, game theory has provided insights into the behavior of distributed systems by modeling the players as utility-maximizing agents. In particular, it has been shown that selfishness causes many systems to perform in a globally suboptimal fashion. Such systems are said to have a large price of anarchy. In this article, we extend this field of research by allowing some players to be malicious rather than selfish. What, we ask, is the impact of malicious players on the system consisting of otherwise selfish players? In particular, we introduce the price of malice as a measure that captures how much the system's efficiency degrades in the presence of malicious players, compared to a purely selfish environment. As a specific example, we analyze the price of malice of a game that models the containment of the spread of viruses. In this game, each player or node can choose whether to install antivirus software. Then, a virus starts from a random node and recursively infects all neighboring nodes that are not inoculated. We establish various results about this game. For instance, we quantify how much the presence of malicious players can deteriorate or—in case of highly risk-averse selfish players—improve the social welfare of the distributed system.

[1]  Michael Dahlin,et al.  FlightPath: Obedience vs. Choice in Cooperative Services , 2008, OSDI.

[2]  Maria J. Serna,et al.  Analysing Orchestrations Using Risk Profiles And Angel-Daemon Games , 2008, CoreGRID Integration Workshop.

[3]  Christos H. Papadimitriou,et al.  Worst-case Equilibria , 1999, STACS.

[4]  Chiu-Yuen Koo,et al.  Broadcast in radio networks tolerating byzantine adversarial behavior , 2004, PODC '04.

[5]  Danny Dolev,et al.  Distributed computing meets game theory: robust mechanisms for rational secret sharing and multiparty computation , 2006, PODC '06.

[6]  Omer Reingold,et al.  Fault tolerance in large games , 2008, EC '08.

[7]  Moshe Babaioff,et al.  Congestion games with malicious players , 2007, EC '07.

[8]  Stefan Schmid,et al.  When selfish meets evil: byzantine players in a virus inoculation game , 2006, PODC '06.

[9]  K. Eliaz Fault Tolerant Implementation , 2002 .

[10]  J. Morgan,et al.  The Spite Motive and Equilibrium Behavior in Auctions , 2003 .

[11]  Sam Toueg,et al.  Simulating authenticated broadcasts to derive simple fault-tolerant algorithms , 1987, Distributed Computing.

[12]  Leslie Lamport,et al.  Reaching Agreement in the Presence of Faults , 1980, JACM.

[13]  Alessandro Vespignani,et al.  Immunization of complex networks. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.

[14]  Michael Dahlin,et al.  BAR fault tolerance for cooperative services , 2005, SOSP '05.

[15]  James Aspnes,et al.  Inoculation strategies for victims of viruses and the sum-of-squares partition problem , 2005, SODA '05.

[16]  Stefan Schmid,et al.  Manipulation in Games , 2007, ISAAC.

[17]  Joseph Y. Halpern,et al.  Ra-tional secret sharing and multiparty computation , 2004, STOC 2004.

[18]  Miguel Oom Temudo de Castro,et al.  Practical Byzantine fault tolerance , 1999, OSDI '99.

[19]  Stefan Schmid,et al.  On the windfall of friendship: inoculation strategies on social networks , 2008, EC '08.

[20]  Sébastien Tixeuil,et al.  An exercise in selfish stabilization , 2008, TAAS.

[21]  Boaz Patt-Shamir,et al.  Collaboration of untrusting peers with changing interests , 2004, EC '04.

[22]  Danny Dolev,et al.  The Byzantine Generals Strike Again , 1981, J. Algorithms.

[23]  Deeparnab Chakrabarty,et al.  The Effect of Malice on the Social Optimum in Linear Load Balancing Games , 2009, ArXiv.

[24]  Boaz Patt-Shamir,et al.  Adaptive Collaboration in Peer-to-Peer Systems , 2005, 25th IEEE International Conference on Distributed Computing Systems (ICDCS'05).

[25]  Po-An Chen,et al.  Bayesian Auctions with Friends and Foes , 2009, SAGT.

[26]  Michael K. Reiter,et al.  Byzantine quorum systems , 1997, STOC '97.

[27]  Jens Grossklags,et al.  Blue versus Red: Towards a Model of Distributed Security Attacks , 2009, Financial Cryptography.

[28]  Tim Roughgarden,et al.  Stackelberg scheduling strategies , 2001, STOC '01.

[29]  Michael Dahlin,et al.  BAR primer , 2008, 2008 IEEE International Conference on Dependable Systems and Networks With FTCS and DCC (DSN).

[30]  Leslie Lamport,et al.  The Byzantine Generals Problem , 1982, TOPL.

[31]  Michael Dahlin,et al.  BAR gossip , 2006, OSDI '06.

[32]  Josep Díaz,et al.  On the Power of Mediators , 2009, WINE.

[33]  Andrew Chi-Chih Yao,et al.  Protocols for secure computations , 1982, FOCS 1982.

[34]  George Karakostas,et al.  Equilibria for Networks with Malicious Users , 2003, ISAAC.

[35]  N. Ling The Mathematical Theory of Infectious Diseases and its applications , 1978 .

[36]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[37]  Yoav Shoham,et al.  Spiteful Bidding in Sealed-Bid Auctions , 2007, IJCAI.

[38]  Christos H. Papadimitriou,et al.  Selfish caching in distributed systems: a game-theoretic analysis , 2004, PODC '04.

[39]  George Karakostas,et al.  Equilibria for networks with malicious users , 2007, ISAAC.

[40]  Aaron Roth,et al.  The Price of Malice in Linear Congestion Games , 2008, WINE.

[41]  Marc Lelarge,et al.  Economic Incentives to Increase Security in the Internet: The Case for Insurance , 2009, IEEE INFOCOM 2009.