Interactive Secure Function Computation

Data, Deepesh; Kurri, Gowtham R.; Ravi, Jithin; Prabhakaran, Vinod M.

doi:10.1109/tit.2020.2980789

Cited by 5 publications

(4 citation statements)

References 58 publications

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“…We say that a tuple (p XY , f 1 (x, y), f 2 (x, y), g(x, y)) is perfectly securely computable in r rounds if there exists a protocol Π n with n = 1 such that (17)-( 18) are satisfied with = 0. It is worthwhile to remark here that, in this setting, asymptotically secure computability is not equivalent to perfectly secure computability as pointed out in our earlier work [12,Remark 2].…”

Section: Privacy Against An Eavesdroppermentioning

confidence: 88%

“…Instead of functions of inputs, suppose if the inputs and outputs of users need to be hidden, our earlier work [12,Theorem 1] shows that asymptotically secure computability is equivalent to perfectly secure computability even with interactive communication. However, this is not true for all secure computation problems.…”

Section: B Non-interactive Setting With Input Distributionmentioning

confidence: 99%

“…However, this is not true for all secure computation problems. In particular, as [12,Remark 2] shows, this is not true for the problem of function computation with privacy against an eavesdropper studied by Tyagi et al [13].…”

Section: B Non-interactive Setting With Input Distributionmentioning

confidence: 99%

“…Narayan et al [11] gave an alternate characterization using common randomness generated by interactive deterministic secure protocols. Data et al [12] gave single-letter characterizations of feasibility and optimal communication rates for two-user interactive secure randomized function computation.…”

Section: Introductionmentioning

confidence: 99%

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Secure Computation to Hide Functions of Inputs

Kurri¹,

Prabhakaran²

2020

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We consider a two-user secure computation problem in which Alice and Bob communicate interactively in order to compute some deterministic functions of the inputs. The privacy requirement is that each user should not learn any additional information about a function of the inputs other than what can be inferred from its own input and output. For the distributionfree setting, i.e., when the protocol must be correct and private for any joint input distribution, we completely characterize the set of all securely computable functions. When privacy is required only against Bob who computes a function based on a single transmission from Alice, we show that asymptotically secure computability is equivalent to perfectly secure computability. Separately, we consider an eavesdropper who has access to all the communication and should not learn any information about some function of the inputs (possibly different from the functions to be computed by the users) and show that interaction may be necessary for secure computation.

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Section: Privacy Against An Eavesdroppermentioning

confidence: 88%

Section: B Non-interactive Setting With Input Distributionmentioning

confidence: 99%