June 20, 2021

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Preventing Manipulation Attack in Local Differential Privacy using Verifiable Randomization Mechanism. (arXiv:2104.06569v2 [cs.CR] UPDATED)

Several randomization mechanisms for local differential privacy (LDP) (e.g.,
randomized response) are well-studied to improve the utility. However, recent
studies show that LDP is generally vulnerable to malicious data providers in
nature. Because a data collector has to estimate background data distribution
only from already randomized data, malicious data providers can manipulate
their output before sending, i.e., randomization would provide them plausible
deniability. Attackers can skew the estimations effectively since they are
calculated by normalizing with randomization probability defined in the LDP
protocol, and can even control the estimations. In this paper, we show how we
prevent malicious attackers from compromising LDP protocol. Our approach is to
utilize a verifiable randomization mechanism. The data collector can verify the
completeness of executing an agreed randomization mechanism for every data
provider. Our proposed method completely protects the LDP protocol from
output-manipulations, and significantly mitigates the expected damage from
attacks. We do not assume any specific attacks, and it works effectively
against general output-manipulation, and thus is more powerful than previously
proposed countermeasures. We describe the secure version of three
state-of-the-art LDP protocols and empirically show they cause acceptable
overheads according to several parameters.