Security, Privacy, and Applied Cryptography Engineering: 11th International Conference, SPACE 2021, Kolkata, India, December 10–13, 2021, Proceedings (Security and Cryptology)

This book constitutes the refereed proceedings of the 11th International Conference on Security, Privacy, and Applied Cryptography Engineering, SPACE 2021, held in Kolkata, India, in December 2021. The 13 full papers presented were carefully reviewed and selected from 42 submissions. This annual event is devoted to various aspects of security, privacy, applied cryptography, and cryptographic engineering. This is a very challenging field, requiring the expertise from diverse domains, ranging from mathematics to solid-state circuit design. Preface Organization Contents Symmetric Cryptography Computing the Distribution of Differentials over the Non-linear Mapping Chi 1 Introduction 2 Preliminary 2.1 Circular Strings 2.2 Differential Probability and Restriction Weight 2.3 Chi Mappings 3 Number of Differentials in chin with Given Weight 3.1 Computing N3(L,H,R) 3.2 Computing N5(L,H,R,Y,X) 4 Experimental Results 4.1 257-Bit State as in Subterranean 4.2 384-Bit State as in Xoodoo 4.3 400-Bit State as in Keccak-f[400] 5 Conclusions References Light-OCB: Parallel Lightweight Authenticated Cipher with Full Security 1 Introduction 1.1 Parallel Authenticated Encryption 1.2 LOCUS-AEAD 1.3 Our Contribution 1.4 Applications and Use Cases 1.5 Light-OCB in DSCI Light-Weight Competition 2 Preliminaries 2.1 Notations and Conventions 2.2 (Ideal) Tweakable Blockcipher 2.3 Authenticated Encryption in the Ideal Cipher Model 2.4 Coefficients-H Technique 3 Specification 3.1 Light-OCB Mode 3.2 Features 3.3 Recommended Instantiation 3.4 Design Rationale 3.5 Light-OCB vs LOCUS-AEAD 4 Security Analysis of Light-OCB 4.1 Privacy Security of Light-OCB 4.2 INT-CTXT Security of Light-OCB 5 Hardware Implementation 5.1 Clock Cycle Analysis 5.2 Hardware Architecture 5.3 Implementation Results 5.4 Benchmarking References MILP Based Differential Attack on Round Reduced WARP 1 Introduction 2 Description of WARP 2.1 Encryption Algorithm 3 Differential Characteristics Search in WARP 3.1 Differential Cryptanalysis 3.2 MILP Modeling to Search the Differential Characteristics 3.3 Differential Characteristics for 18-Round WARP 3.4 Differential Characteristics for 19-Round WARP 4 Key Recovery Attack on 21-Round WARP 4.1 Data Collection 4.2 Key Recovery 4.3 Complexity 5 Conclusion Appendices A Differential Characteristics (108) of 18-Round WARP with Probability of 2-122 References Post-Quantum Cryptography and Homomorphic Encryption SHELBRS: Location-Based Recommendation Services Using Switchable Homomorphic Encryption 1 Introduction 2 Related Work 3 Preliminaries 3.1 Hilbert Curve 3.2 Collaborative Filtering (CF) Recommender Based on Co-occurrence Matrix (CM) 3.3 Partially Homomorphic Encryption (PHE) 3.4 Switchable Homomorphic Encryption (SHE) 4 Lyu et al.'s Protocol 4.1 System Model 4.2 Description of Lyu et al.'s Protocol 5 Proposed SHELBRS Protocol 5.1 Setup Stage 5.2 Initialization Stage 5.3 Protocol Operation Stage 6 Experimental Results 6.1 Security Analysis 7 Conclusions and Future Work References On Threat of Hardware Trojan to Post-Quantum Lattice-Based Schemes: A Key Recovery Attack on SABER and Beyond 1 Introduction 2 Preliminaries 2.1 Notation 2.2 Generic Framework for LWE/LWR-Based PKE 2.3 Practical CCAs on IND-CCA Secure KEMs 3 PC Oracle-Based CCA on LWE/LWR-Based KEMs 3.1 PC Oracle-Based CCA 3.2 Parallelized PC Oracle-Based CCA 4 HT-Assisted Key Recovery Attack 4.1 Adversary Model 4.2 Intuition 4.3 Applicability of Binary/Parallel Oracle-Based CCA 5 HT Design Methodology 5.1 Design of HT Trigger Mechanism 5.2 Design of HT Payload 6 Implementation Details 6.1 Implementing HT Trigger 6.2 Implementing HT Payload 6.3 Improved HT Design 6.4 Implementation Results 7 On the Applicability of HT Detection Techniques 7.1 White Box IP 8 Conclusion A Chosen Ciphertexts for Key Recovery in Kyber References Safe-Error Attacks on SIKE and CSIDH 1 Introduction 2 Background 2.1 SIKE 2.2 CSIDH 2.3 Safe-Error Attacks 3 Attacks on SIKE 3.1 M-Safe Attack on SIKE 3.2 C-Safe Attack on SIKE 4 Attacks on CSIDH 4.1 M Safe-Error Attack on an Implementation Using One Point 4.2 M Safe-Error Attacks on an Implementation Using Two Points 5 Practical Experiments 5.1 Attacks on SIKE 5.2 Attacks on CSIDH 6 Countermeasures 6.1 Securing SIKE 6.2 Securing CSIDH 7 Conclusion References Hardware Security and Side-Channel Attacks Network Data Remanence Side Channel Attack on SPREAD, H-SPREAD and Reverse AODV 1 Introduction 2 Background 2.1 Working Principle of Secret Sharing (SS) Scheme 2.2 Working Principle of MSSS 2.3 Network Data Remanence Side-Channel 2.4 NDR Attacks on MSSS 3 Working Principle of SPREAD and H-SPREAD 4 Proposed NDR Side Channel on SPREAD and H-SPREAD 5 Working Principle of Path Hopping Based on Reverse AODV for Security (PHR-AODV) 6 Proposed NDR Side Channel Attack on PHR-AODV 7 Experimental Setup and Results 8 Conclusion References Parasite: Mitigating Physical Side-Channel Attacks Against Neural Networks 1 Introduction 1.1 Threat Scenario 2 Related Works 3 Background 3.1 Neural Networks 3.2 Correlation Electromagnetic Analysis 3.3 Side-Channel Attacks on Neural Networks 4 Adding Parasitic Layers 4.1 Proposal Overview 4.2 Proposal Description 4.3 Approximating the Identity Function 5 Evaluation 5.1 Simulation 5.2 Models Considered 5.3 Results 6 Discussions 6.1 Number of Traces to Recover the Weights 6.2 Increasing the Entropy of the Added Noise 6.3 Approximating the ReLU Activation Function 6.4 Improving the CNNs at Hand 6.5 Comparing to Common Countermeasures 7 Conclusion A Pearson Correlation B Weight Distribution References Reinforcement Learning-Based Design of Side-Channel Countermeasures 1 Introduction 2 Preliminaries 2.1 Deep Learning and Profiling Side-Channel Analysis 2.2 Side-Channel Countermeasures 2.3 Datasets and Leakage Models 2.4 Reinforcement Learning 3 Related Works 4 the RL-Based Countermeasure Selection Framework 4.1 General Setup 4.2 Reward Functions 5 Experimental Results 5.1 ASCAD Fixed Key Dataset 5.2 ASCAD Random Keys Dataset 6 Conclusions and Future Work A Q-Learning Performance for the ASCAD with Random Keys Dataset References Deep Freezing Attacks on Capacitorspg and Electronic Circuits 1 Introduction 1.1 Paper Organization 2 Background 2.1 Temperature Characteristics of Capacitors 2.2 Cold Boot and Chill Out Attacks 3 Threat Model 4 Experimental Setup 4.1 Liquid Nitrogen Freezing Approach 5 Capacitor Freezing Attacks 6 Electronic Filter Freezing Attacks 6.1 Attacks on High-Pass Filters 6.2 Attacks on Low-Pass Filters 6.3 Attacks on Higher-Order Filters 6.4 Comparison to Freezing with Cooling Sprays 7 Energy Storage Freezing Attacks 7.1 Capacitor-Powered MSP430-class MCUs 7.2 Setup for Microcrontroller Freezing Attacks 7.3 Freezing Attacks on Energy Storage in Electrolytic Capacitors 7.4 Freezing Attacks on Energy Storage in Supercapacitors 8 Discussion 8.1 Alternative Polarized Capacitors 8.2 Larger Capacitors 8.3 Added Insulation 8.4 Temperature Sensitive Packaging 8.5 Temperature Sensors 9 Conclusion References AI and Cloud Security Encrypted SQL Arithmetic Functions Processing for Secure Cloud Database 1 Introduction 2 Prior Works 3 Preliminaries: Homomorphic Encryption(HE) 4 Designing SQL Associated Arithmetic Functions in Encrypted Domain 4.1 ABS() Function 4.2 CEILING() and FLOOR() Function 4.3 SIGN() Function 4.4 SQUARE() Function 4.5 EXP() and LOG() Function 4.6 POWER() Function 4.7 SQRT() Function 4.8 Encrypted SQL Query with Mathematical Function 5 Performance Analysis 6 Conclusion References Robustness Against Adversarial Attacks Using Dimensionality 1 Introduction 1.1 Motivation 1.2 Contribution 1.3 Organization 2 Adversarial Attacks 2.1 Formulation 2.2 Curse of Dimensionality 2.3 Attack and Defences Review 3 Defence Design 3.1 Parallel Pathways 3.2 Detecting Adversarial Samples 3.3 Dimensionality 4 Implementation 4.1 Pipeline 4.2 Models 4.3 Dimension Reduction 5 Experiments 5.1 Design 5.2 Results 5.3 Key Findings 6 Concluding Remarks References SoK - Network Intrusion Detection on FPGA 1 Introduction 2 Pattern Matching-Based Network Intrusion Detection 3 Flow Measurement-Based Network Intrusion Detection 4 Machine Learning-Based Network Intrusion Detection 4.1 Machine Learning for Network Intrusion Detection 4.2 Traditional Machine Learning Algorithms on FPGA 4.3 Deep Learning on FPGA 4.4 Discussion 5 Main Takeaways 5.1 Properties of the Compared NIDS Approaches 5.2 Limitations and Challenges of State-of-the-Art NIDSs 6 Conclusion References Author Index