BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning

Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many benchmark frameworks and results under standard experimental settings are available. Compared to them, however, CL methods for graph data (graph CL) are relatively underexplored because of (a) the lack of standard experimental settings, especially regarding how to deal with the dependency between instances, (b) the lack of benchmark datasets and scenarios, and (c) high complexity in implementation and evaluation due to the dependency. In this paper, regarding (a) we define four standard incremental settings (task-, class-, domain-, and time-incremental) for node-, link-, and graph-level problems, extending the previously explored scope. Regarding (b), we provide 31 benchmark scenarios based on 20 real-world graphs. Regarding (c), we develop BeGin, an easy and fool-proof framework for graph CL. BeGin is easily extended since it is modularized with reusable modules for data processing, algorithm design, and evaluation. Especially, the evaluation module is completely separated from user code to eliminate potential mistakes. Regarding benchmark results, we cover 3X more combinations of incremental settings and levels of problems than the latest benchmark. All assets for the benchmark framework are publicly available at https://github.com/ShinhwanKang/BeGin

AIM: Symmetric Primitive for Shorter Signatures with Stronger Security (Full Version)

Post-quantum signature schemes based on the MPC-in-the-Head (MPCitH) paradigm are recently attracting significant attention as their security solely depends on the one-wayness of the underlying primitive, providing diversity for the hardness assumption in post-quantum cryptography. Recent MPCitH-friendly ciphers have been designed using simple algebraic S-boxes operating on a large field in order to improve the performance of the resulting signature schemes. Due to their simple algebraic structures, their security against algebraic attacks should be comprehensively studied. In this paper, we refine algebraic cryptanalysis of power mapping based S-boxes over binary extension fields, and cryptographic primitives based on such S-boxes. In particular, for the Gröbner basis attack over $\mathbb{F}_2$, we experimentally show that the exact number of Boolean quadratic equations obtained from the underlying S-boxes is critical to correctly estimate the theoretic complexity based on the degree of regularity. Similarly, it turns out that the XL attack might be faster when all possible quadratic equations are found and used from the S-boxes. This refined cryptanalysis leads to more precise algebraic analysis of cryptographic primitives based on algebraic S-boxes. Considering the refined algebraic cryptanalysis, we propose a new one-way function, dubbed $\mathsf{AIM}$, as an MPCitH-friendly symmetric primitive with high resistance to algebraic attacks. The security of $\mathsf{AIM}$ is comprehensively analyzed with respect to algebraic, statistical, quantum, and generic attacks. $\mathsf{AIM}$ is combined with the BN++ proof system, yielding a new signature scheme, dubbed $\mathsf{AIMer}$. Our implementation shows that $\mathsf{AIMer}$ outperforms existing signature schemes based on symmetric primitives in terms of signature size and signing time

Hybrid WBC: Secure and Efficient White-Box Encryption Schemes

White-box cryptography aims at providing security against an adversary that has access to the encryption process. Numerous white-box encryption schemes were proposed since the introduction of white-box cryptography by Chow et al. in 2002. However, most of them are slow, and thus, can be used in practice only to protect very small amounts of information, such as encryption keys. In this paper we present a new threat model for white-box cryptography which corresponds to the practical abilities of the adversary in a wide range of applications. Furthermore, we study design criteria for white-box primitives that are important from the industry point of view. Finally, we propose a class of new primitives that combine a white-box algorithm with a standard block cipher to obtain white-box protection for encrypting long messages, with high security and reasonable performance

Association between use of hydrochlorothiazide and nonmelanoma skin cancer: Common data model cohort study in Asian population

Although hydrochlorothiazide (HCTZ) has been suggested to increase skin cancer risk in white Westerners, there is scant evidence for the same in Asians. We analyzed the association between the use of hydrochlorothiazide and non-melanoma in the Asian population using the common data model. METHODS: A retrospective multicenter observational study was conducted using a distributed research network to analyze the effect of HCTZ on skin cancer from 2004 to 2018. We performed Cox regression to evaluate the effects by comparing the use of HCTZ with other antihypertensive drugs. All analyses were re-evaluated using matched data using the propensity score matching (PSM). Then, the overall effects were evaluated by combining results with the meta-analysis. RESULTS: Positive associations were observed in the use of HCTZ with high cumulative dose for non-melanoma skin cancer (NMSC) in univariate analysis prior to the use of PSM. Some negative associations were observed in the use of low and medium cumulative doses. CONCLUSION: Although many findings in our study were inconclusive, there was a non-significant association of a dose-response pattern with estimates increasing in cumulative dose of HCTZ. In particular, a trend with a non-significant positive association was observed with the high cumulative dose of HCTZ

Transciphering Framework for Approximate Homomorphic Encryption (Full Version)

Homomorphic encryption (HE) is a promising cryptographic primitive that enables computation over encrypted data, with a variety of applications including medical, genomic, and financial tasks. In Asiacrypt 2017, Cheon et al. proposed the CKKS scheme to efficiently support approximate computation over encrypted data of real numbers. HE schemes including CKKS, nevertheless, still suffer from slow encryption speed and large ciphertext expansion compared to symmetric cryptography. In this paper, we propose a novel hybrid framework, dubbed RtF (Real-to-Finite-field) framework, that supports CKKS. The main idea behind this construction is to combine the CKKS and the FV homomorphic encryption schemes, and use a stream cipher using modular arithmetic in between. As a result, real numbers can be encrypted without significant ciphertext expansion or computational overload on the client side. As an instantiation of the stream cipher in our framework, we propose a new HE-friendly cipher, dubbed HERA, and extensively analyze its security and efficiency. The main feature of HERA is that it uses a simple randomized key schedule. Compared to recent HE-friendly ciphers such as FLIP and Rasta using randomized linear layers, HERA requires a smaller number of random bits. For this reason, HERA significantly outperforms existing HE-friendly ciphers on both the client and the server sides. With the RtF transciphering framework combined with HERA at the 128-bit security level, we achieve small ciphertext expansion ratio with a range of 1.23 to 1.54, which is at least 23 times smaller than using (symmetric) CKKS-only, assuming the same precision bits and the same level of ciphertexts at the end of the framework. We also achieve 1.6 $\mu$s and 21.7 MB/s for latency and throughput on the client side, which are 9085 times and 17.8 times faster than the CKKS-only environment, respectively