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Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems based on Sample Identifying Complexity and Deciphering Time

Kaoru Teranishi, Tomonori Sadamoto, Aranya Chakrabortty, Kiminao Kogiso

TL;DR

This article first connects the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective and proposes a systematic method for designing both of an optimal key length to prevent system identification with a given precision within a given life span of systems.

Abstract

In the state-of-the-art literature on cryptography and control theory, there has been no systematic methodology of constructing cyber-physical systems that can achieve desired control performance while being protected against eavesdropping attacks. In this paper, we tackle this challenging problem. We first propose two novel notions referred to as sample identifying complexity and sample deciphering time in an encrypted-control framework. The former explicitly captures the relation between the dynamical characteristics of control systems and the level of identifiability of the systems while the latter shows the relation between the computation time for the identification and the key length of a cryptosystem. Based on these two tractable new notions, we propose a systematic method for designing the both of an optimal key length to prevent system identification with a given precision within a given life span of systems, and of an optimal controller to maximize both of the control performance and the difficulty of the identification. The efficiency of the proposed method in terms of security level and realtime-ness is investigated through numerical simulations. To the best of our knowledge, this paper first connect the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective.

Designing Optimal Key Lengths and Control Laws for Encrypted Control Systems based on Sample Identifying Complexity and Deciphering Time

TL;DR

This article first connects the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective and proposes a systematic method for designing both of an optimal key length to prevent system identification with a given precision within a given life span of systems.

Abstract

In the state-of-the-art literature on cryptography and control theory, there has been no systematic methodology of constructing cyber-physical systems that can achieve desired control performance while being protected against eavesdropping attacks. In this paper, we tackle this challenging problem. We first propose two novel notions referred to as sample identifying complexity and sample deciphering time in an encrypted-control framework. The former explicitly captures the relation between the dynamical characteristics of control systems and the level of identifiability of the systems while the latter shows the relation between the computation time for the identification and the key length of a cryptosystem. Based on these two tractable new notions, we propose a systematic method for designing the both of an optimal key length to prevent system identification with a given precision within a given life span of systems, and of an optimal controller to maximize both of the control performance and the difficulty of the identification. The efficiency of the proposed method in terms of security level and realtime-ness is investigated through numerical simulations. To the best of our knowledge, this paper first connect the relationship between the security of cryptography and dynamical systems from a control-theoretic perspective.

Paper Structure

This paper contains 30 sections, 14 theorems, 54 equations, 7 figures, 1 table, 1 algorithm.

Key Result

Lemma 1

Consider the system in Fig. fig:scenario under the attack in Definition def:adversary. Suppose $A$ in eq:system is Schur. Then, the parameters of a posterior probability $p(A|\mathcal{D})$ in Definition def:adversary are described as Besides, the following relations hold:

Figures (7)

  • Figure 1: Attack scenario and actions of adversary and defender.
  • Figure 2: Schematic pictures of sample identifying-complexity curve $\gamma(T,F)$ and sample deciphering-time curve $\tau(T,k)$.
  • Figure 3: Result of Bayesian estimation for system matrix.
  • Figure 4: Comparison of sample identifying-complexity curves.
  • Figure 5: Comparison of sample deciphering-time curves.
  • ...and 2 more figures

Theorems & Definitions (50)

  • Definition 1
  • Definition 2: negligible function Katz15
  • Definition 3
  • Definition 4
  • Definition 5
  • Remark 1
  • Remark 2
  • Remark 3
  • Remark 4
  • Lemma 1
  • ...and 40 more