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Controllable Privacy Preserving Blockchain

FiatChain: Distributed Privacy Preserving Cryptocurrency with Law Enforcement Capabilities

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Cyber Security Cryptography and Machine Learning (CSCML 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11527))

Abstract

Central banks are reluctant to accept cryptocurrency, because current implementations of decentralized privacy preserving transactions make it impossible to apply know your customer (KYC) and anti-money laundering (AML) procedures. In this paper, we augment a distributed privacy preserving cyptocurrency known as Monero with KYC and AML procedures. The proposed solution relies on secretly sharing of the clients’ private view keys and private transaction keys among a large number of permissioned signers (PSs). The resulting cryptocurrency maintains the notion of distributed trust while allowing a group of PSs to cooperate, collectively applying KYC and AML procedures.

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Notes

  1. 1.

    This scheme is implemented on the SPDZ arithmetic circuit because of implementation details.

  2. 2.

    According to the implementation the exact term is \( P = H_s (8\cdot rA || i)G+B\), where multiplying by eight forces the rA point to be in the group base point G and || denotes byte concatenation with output index (oi). The output index prevents the generation of multiple identical stealth addresses for the same receiver (which will prevent the receiver from spending more than one output).

  3. 3.

    The value (A, B) is translated into the x-term of that function.

  4. 4.

    https://keccak.team/keccak_specs_summary.html.

  5. 5.

    The integrity of the result is guaranteed and malicious parties deviating from the protocol during execution should not be able to force honest parties to output an incorrect result.

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Authors and Affiliations

  1. Telekom Innovation Laboratories, Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, P.O.B. 653, Beer-Sheva, Israel

    Rami Puzis, Guy Barshap, Polina Zilberman & Oded Leiba

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  1. Rami Puzis
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  2. Guy Barshap
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  3. Polina Zilberman
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  4. Oded Leiba
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Correspondence to Rami Puzis .

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Editors and Affiliations

  1. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Shlomi Dolev

  2. Ben-Gurion University of the Negev, Beer-Sheva, Israel

    Danny Hendler

  3. Tata Consultancy Services, Chennai, India

    Sachin Lodha

  4. Columbia University and Google, New York, NY, USA

    Moti Yung

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Puzis, R., Barshap, G., Zilberman, P., Leiba, O. (2019). Controllable Privacy Preserving Blockchain. In: Dolev, S., Hendler, D., Lodha, S., Yung, M. (eds) Cyber Security Cryptography and Machine Learning. CSCML 2019. Lecture Notes in Computer Science(), vol 11527. Springer, Cham. https://doi.org/10.1007/978-3-030-20951-3_16

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  • DOI: https://doi.org/10.1007/978-3-030-20951-3_16

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