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Towards Tight Adaptive Security of Non-interactive Key Exchange

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Theory of Cryptography (TCC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 13044))

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Abstract

We investigate the quality of security reductions for non-interactive key exchange (NIKE) schemes. Unlike for many other cryptographic building blocks (like public-key encryption, signatures, or zero-knowledge proofs), all known NIKE security reductions to date are non-tight, i.e., lose a factor of at least the number of users in the system. In that sense, NIKE forms a particularly elusive target for tight security reductions.

The main technical obstacle in achieving tightly secure NIKE schemes are adaptive corruptions. Hence, in this work, we explore security notions and schemes that lie between selective security and fully adaptive security. Concretely:

We exhibit a tradeoff between key size and reduction loss. We show that a tighter reduction can be bought by larger public and secret NIKE keys. Concretely, we present a simple NIKE scheme with a reduction loss of \(O(N^2\log (\nu )/\nu ^2)\), and public and secret keys of \(O(\nu )\) group elements, where \(N\) denotes the overall number of users in the system, and \(\nu \) is a freely adjustable scheme parameter.

Our scheme achieves full adaptive security even against multiple “test queries” (i.e., adversarial challenges), but requires keys of size \(O(N)\) to achieve (almost) tight security under the matrix Diffie-Hellman assumption. Still, already this simple scheme circumvents existing lower bounds.

We show that this tradeoff is inherent. We contrast the security of our simple scheme with a lower bound for all NIKE schemes in which shared keys can be expressed as an “inner product in the exponent”. This result covers the original Diffie-Hellman NIKE scheme, as well as a large class of its variants, and in particular our simple scheme. Our lower bound gives a tradeoff between the “dimension” of any such scheme (which directly corresponds to key sizes in existing schemes), and the reduction quality. For \(\nu =O(N)\), this shows our simple scheme and reduction optimal (up to a logarithmic factor).

We exhibit a tradeoff between security and key size for tight reductions. We show that it is possible to circumvent the inherent tradeoff above by relaxing the desired security notion. Concretely, we consider the natural notion of semi-adaptive security, where the adversary has to commit to a single test query after seeing all public keys. As a feasibility result, we bring forward the first scheme that enjoys compact public keys and tight semi-adaptive security under the conjunction of the matrix Diffie-Hellman and learning with errors assumptions.

We believe that our results shed a new light on the role of adaptivity in NIKE security, and also illustrate the special role of NIKE when it comes to tight security reductions.

D. Hofheinz and R. Langrehr—Supported in part by ERC CoG grant 724307.

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Notes

  1. 1.

    In this work, we focus on the public-key setting, i.e., we assume a public-key infrastructure. We note, however, that NIKE has also been considered in the identity-based setting [17, 33, 38].

  2. 2.

    This means that we can currently only map NIKE adversaries with success probability \(\varepsilon \) and runtime \(t\) to adversaries on a suitable computational assumption with runtime \(t'\approx t\) but success probability no more than \(\varepsilon '\approx \varepsilon /N\).

  3. 3.

    In a nutshell, the meta-reduction extracts enough shared keys from \(\varLambda \) to take the role of a successful adversary in a rewound \(\varLambda \)-instance. If \(\varLambda \) is “too successful”, this causes \(\varLambda \) to solve the underlying computational problem with these extracted keys. Hence, \(\varLambda \) solves the underlying problem essentially by interacting with itself.

  4. 4.

    For our results we require uniqueness of a corresponding public key vector given the public key, which holds for all DH-based schemes from the literature including our first NIKE.

  5. 5.

    To capture adversaries with arbitrary success probability \(\varepsilon _\mathcal {A} \), the hypothetical adversary can simply flip a biased coin and only output the shared key with probability \(\varepsilon _\mathcal {A} \).

  6. 6.

    Even though only one secret key is necessary to compute the shared key, we can only be sure that the reduction is committed to the shared key when given both secret keys, since the reduction could switch to a semi-functional public key (without valid secret key).

  7. 7.

    This is a slight oversimplification. In fact, programming requires to also make public keys semi-functional, as in the security proof of \({\mathtt {NIKE}} _\mathsf {ip}\) sketched above. Our formal programmability definition will allow for such adjustments during programming.

  8. 8.

    This is again an oversimplification: for a particular choice of tag, one involved party \(P_i\) will not be able to compute the TNIKE shared key, while the other party \(P_j\) will be able to compute a shared key that depends on entropy in \(P_j\)’s secret key.

  9. 9.

    In this overview, we neglect the fact that \(\tau \) should be a small scalar. Our full scheme will actually encrypt \(\tau \) bitwise.

  10. 10.

    We note that to obtain tight security at this point, we will temporarily switch the used FHE scheme into a lossy mode of encryption [3, 22].

  11. 11.

    They correctly prove that \(\mathcal {U} _{\ell ,k}\hbox {-}\mathsf {MDDH}\) is tightly equivalent to \(\mathcal {U} _{k}\hbox {-}\mathsf {MDDH}\), but the proof can not show that Q-fold \(\mathcal {U} _{\ell ,k}\hbox {-}\mathsf {MDDH}\) is tightly equivalent to Q-fold \(\mathcal {U} _{k}\hbox {-}\mathsf {MDDH}\).

  12. 12.

    For simplicity of this explanation we assume for now that such a set of keys exists, but stress that our results do not rely on it.

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Hesse, J., Hofheinz, D., Kohl, L., Langrehr, R. (2021). Towards Tight Adaptive Security of Non-interactive Key Exchange. In: Nissim, K., Waters, B. (eds) Theory of Cryptography. TCC 2021. Lecture Notes in Computer Science(), vol 13044. Springer, Cham. https://doi.org/10.1007/978-3-030-90456-2_10

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