Introduces a pseudo-metric to quantify advice usefulness and shows reliable advice enables efficient approximate Stackelberg strategies while unreliable advice blocks simultaneous near-Stackelberg and no-regret guarantees but permits weak dominance in some correlated equilibria.
Exploiting Social Navigation
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abstract
We present an effective Sybil attack against social location based services. Our attack is based on creating a large number of reputed "bot drivers", and controlling their reported locations using fake GPS reports. We show how this attack can be used to influence social navigation systems by applying it to Waze - a prominent social navigation application used by over 50 million drivers. We show that our attack can fake traffic jams and dramatically influence routing decisions. We present several techniques for preventing the attack, and show that effective mitigation likely requires the use of additional carrier information.
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cs.GT 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Leveraging Machine-Learned Advice in Strategic Interactions with No-Regret Learners
Introduces a pseudo-metric to quantify advice usefulness and shows reliable advice enables efficient approximate Stackelberg strategies while unreliable advice blocks simultaneous near-Stackelberg and no-regret guarantees but permits weak dominance in some correlated equilibria.