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July 30, 20240 citationsOpen Access

Local-Data-Hiding and Causal Inseparability: Probing Indefinite Causal Structures with Cryptographic Primitives

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SNSahil Gopalkrishna NaikSSSamrat SenRPRam Krishna Patra

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Abstract

Formulation of physical theories typically assumes a definite causal structure -- either static or dynamic -- among the set of physical events. Recent studies, however, suggest the possibility of indefiniteness in causal structure, which emerges as a novel information primitive offering advantages in various protocols. In this work, we explore utilities of this new primitive in cryptographic applications. To this aim, we propose a task called local-data-hiding, where a referee distributes encrypted messages among distant parties in such a way that the parties individually remain completely ignorant about the messages, and thus try to decrypt their respective messages through mutual collaboration. As we demonstrate, agents embedded in an indefinite causal structure can outperform their counterparts operating in a definite causal background. Considering the bipartite local-bit-hiding (LBH) task, we establish a strict duality between its optimal success probability and the optimal violation of a causal inequality obtained from the guess-your-neighbour's-input game. This, in turn, provides a way forward to obtain Tsirelson-type bounds for causal inequalities. Furthermore, similar to Peres's separability criterion, we derive a necessary criterion for quantum processes to be useful in the LBH task. We then report an intriguing super-activation phenomenon, where two quantum processes, each individually not useful for the LBH task, become useful when used together. We also analyze the utility of causal indefiniteness arising in classical setups and show its advantages in multipartite variants of the local-data-hiding task. Along with establishing new cryptographic applications our study illuminates various unexplored aspects of causal indefiniteness, and welcomes further studies on this new information primitive.

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Cite This Study

Naik et al. (2024) studied this question.

synapsesocial.com/papers/68e5e8f6b6db64358757db31https://doi.org/10.48550/arxiv.2407.20543
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