Ruhr-Universität Bochum (RUB): Open Journal Systems
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Molyneux’s question today: Introduction to the special issue
No full textFew topics in the philosophy of perception have received more attention than Molyneux’s question: would a person with congenital blindness, able to identify cubes and spheres by touch, immediately or even eventually identify these shapes by sight alone, if made to see? This special issue focuses on the new developments concerning the answers to this question, as well as on the new questions in the light of the results of the results from the sciences of the mind
The analysis of literary texts Teachers\u27 beliefs in a field of tension in the didactics of literature
Get PDFDie konstruktive Verbindung von Analyse und Interpretation literarischer Texte wird aus literaturdidaktischer Perspektive immer wieder gefordert, die unterrichtliche Praxis stellt sich jedoch nicht selten anders dar. Anhand von Interview-Daten aus dem Projekt „Literarisches Verstehen im Umgang mit Metaphorik: Rekonstruktion von lernerseitigen Verstehensprozessen und lehrerseitigen Modellierungen (LiMet)“ wird in dem Beitrag rekonstruiert, welche gegenstands-, fach- und lernendenbezogenen Überzeugungen entsprechende Praktiken rahmen. Da der Fokus des Projekts auf dem Umgang von Lernenden und Lehrenden mit Metaphorik lag, werden insbesondere die Zusammenhänge zwischen den lehrendenseitigen Modellierungen der Metapher und ihren Überzeugungen zur Analyse literarischer Texte aufgezeigt. Der Beitrag verfolgt zudem das Ziel, anhand dieses Projekts zu reflektieren, welche Ableitungen aus der Rekonstruktion von Überzeugungen möglich sind.The constructive connection of analysis and interpretation of literary texts is constantly demanded from the perspective of literature didactics, but teaching practice often presents itself differently. Based on interview data from the project “Literary Understanding and Metaphor (LiMet)”, the article reconstructs which teaching object-, subject- and learner-related beliefs frame these practices. Since the focus of the project was on how learners and teachers deal with metaphor, the connections between the teachers\u27 modelling of metaphor and their beliefs about the analysis of literary texts will be particularly illustrated. The article also aims to reflect which derivations are possible from the reconstruction of beliefs
Which language-internal factors affect students’ capitalization performance in German?
Get PDFDie satzinterne Großschreibung im Deutschen stellt für viele Schreiber*innen eine große Herausforderung dar. Während schon seit geraumer Zeit der traditionell wortartbezogene Ansatz als Ursache für diese Rechtschreibprobleme diskutiert wird, standen sprachstrukturelle Faktoren, die die Großschreibleistung beeinflussen, bisher wenig im Fokus. In der hier vorgestellten Studie wird die Bedeutung syntaktischer und morphologischer Merkmale von Abstrakta für die Großschreibung systematisch untersucht. Dazu wurden 1024 Testsätze konstruiert, bei denen die morphologische Struktur der Testwörter systematisch variiert und deren syntaktischen Kontexte systematisch kombiniert wurden. Die Testsätze wurden auf acht vergleichbare Testversionen mit jeweils 128 Testsätzen verteilt und 168 Siebtklässler*innen vorgelegt. Die Ergebnisse der Varianzanalysen zeigen, dass sowohl syntaktische als auch morphologische Merkmale die Großschreibleistung beeinflussen: Die Nominalgruppenstruktur, das Vorhandensein nominaler Suffixe sowie die Pluralfähigkeit der Abstrakta haben einen Einfluss auf die Großschreibleistung von Schüler*innen im 7. Schuljahr.In German, sentence-internal capitalization presents a major challenge to writers. Whereas the traditional lexical teaching approach has been under discussion as a potential cause for these orthographic difficulties, the impact of language-internal factors on writers’ performance has received little attention. The present study systematically investigates how syntactic and morphological characteristics of abstract nouns impact capitalization. To achieve this, 1,024 test sentences were constructed, incorporating test words whose morphological structure and syntactic contexts were systematically varied and combined. The test sentences were divided into eight comparable test versions, each containing 128 test sentences, and presented to 168 7th-grade students. The results of the variance analyses reveal that both syntactic and morphological characteristics influence capitalization: the structure of the noun phrase, the presence of nominal suffixes as well the ability of abstract nouns to form plurals affect 7th-grade students’ capitalization performance
Algebraic Attack on FHE-Friendly Cipher HERA Using Multiple Collisions
No full textFully homomorphic encryption (FHE) is an advanced cryptography technique to allow computations (i.e., addition and multiplication) over encrypted data. After years of effort, the performance of FHE has been significantly improved and it has moved from theory to practice. The transciphering framework is another important technique in FHE to address the issue of ciphertext expansion and reduce the client-side computational overhead. To apply the transciphering framework to the CKKS FHE scheme, a new transciphering framework called the Real-to-Finite-Field (RtF) framework and a corresponding FHE-friendly symmetric-key primitive called HERA were proposed at ASIACRYPT 2021. Although HERA has a very similar structure to AES, it is considerably different in the following aspects: 1) the power map x → x3 is used as the S-box; 2) a randomized key schedule is used; 3) it is over a prime field Fp with p > 216. In this work, we perform the first third-party cryptanalysis of HERA, by showing how to mount new algebraic attacks with multiple collisions in the round keys. Specifically, according to the special way to randomize the round keys in HERA, we find it possible to peel off the last nonlinear layer by using collisions in the last-round key and a simple property of the power map. In this way, we could construct an overdefined system of equations of a much lower degree in the key, and efficiently solve the system via the linearization technique. As a esult, for HERA with 192 and 256 bits of security, respectively, we could break some parameters under the same assumption made by designers that the algebra constant ω for Gaussian elimination is ω = 2, i.e., Gaussian elimination on an n × n matrix takes O(nω) field operations. If using more conservative choices like ω ∈ {2.8, 3}, our attacks can also successfully reduce the security margins of some variants of HERA to only 1 round. However, the security of HERA with 80 and 128 bits of security is not affected by our attacks due to the high cost to find multiple collisions. In any case, our attacks reveal a weakness of HERA caused by the randomized key schedule and its small state size
Impossible Boomerang Attacks Revisited: Applications to Deoxys-BC, Joltik-BC and SKINNY
No full textThe impossible boomerang (IB) attack was first introduced by Lu in his doctoral thesis and subsequently published at DCC in 2011. The IB attack is a variant of the impossible differential (ID) attack by incorporating the idea of the boomerang attack. In this paper, we revisit the IB attack, and introduce the incompatibility of two characteristics in boomerang to the construction of an IB distinguisher. With our methodology, all the constructions of IB distinguisher are represented in a unified manner. Moreover, we show that the related-(twea)key IB distinguishers possess more freedom than the ones of ID so that it can cover more rounds.We also propose a new tool based on Mixed-Integer Quadratically-Constrained Programming (MIQCP) to search for IB attacks. To illustrate the power of the IB attack, we mount attacks against three tweakable block ciphers: Deoxys-BC, Joltik-BC and SKINNY. For Deoxys-BC, we propose a related-tweakey IB attack on 14-round Deoxys-BC-384, which improves the best previous related-tweakey ID attack by 2 rounds, and we improve the data complexity of the best previous related-tweakey ID attack on 10-round Deoxys-BC-256. For Joltik-BC, we propose the best attacks against 10-round Joltik-BC-128 and 14-round Joltik-BC-192 with related-tweakey B attack. For SKINNY-n-3n, we propose a 27-round related-tweakey IB attack, which improves both the time and the memory complexities of the best previous ID attack. We also propose the first related-tweakey IB attack on 28-round SKINNY-n-3n, which improves the previous best ID attack by one round
Load-Balanced Parallel Implementation on GPUs for Multi-Scalar Multiplication Algorithm
No full textMulti-scalar multiplication (MSM) is an important building block in most of elliptic-curve-based zero-knowledge proof systems, such as Groth16 and PLONK. Recently, Lu et al. proposed cuZK, a new parallel MSM algorithm on GPUs. In this paper, we revisit this scheme and present a new GPU-based implementation to further improve the performance of MSM algorithm. First, we propose a novel method for mapping scalars into Pippenger’s bucket indices, largely reducing the number of buckets compared to the original Pippenger algorithm. Second, in the case that memory is sufficient, we develop a new efficient algorithm based on homogeneous coordinates in the bucket accumulation phase. Moreover, our accumulation phase is load-balanced, which means the parallel speedup ratio is almost linear growth as the number of device threads increases. Finally, we also propose a parallel layered reduction algorithm for the bucket aggregation phase, whose time complexity remains at the logarithmic level of the number of buckets. The implementation results over the BLS12-381 curve on the V100 graphics card show that our proposed algorithm achieves up to 1.998x, 1.821x and 1.818x speedup compared to cuZK at scales of 221, 222, and 223, respectively
SHAPER: A General Architecture for Privacy-Preserving Primitives in Secure Machine Learning
No full textSecure multi-party computation and homomorphic encryption are two primary security primitives in privacy-preserving machine learning, whose wide adoption is, nevertheless, constrained by the computation and network communication overheads. This paper proposes a hybrid Secret-sharing and Homomorphic encryption Architecture for Privacy-pERsevering machine learning (SHAPER). SHAPER protects sensitive data in encrypted or randomly shared domains instead of relying on a trusted third party. The proposed algorithm-protocol-hardware co-design methodology explores techniques such as plaintext Single Instruction Multiple Data (SIMD) and fine-grained scheduling, to minimize end-to-end latency in various network settings. SHAPER also supports secure domain computing acceleration and the conversion between mainstream privacy-preserving primitives, making it ready for general and distinctive data characteristics. SHAPER is evaluated by FPGA prototyping with a comprehensive hyper-parameter exploration, demonstrating a 94x speed-up over CPU clusters on large-scale logistic regression training tasks
On the (Im)possibility of Preventing Differential Computation Analysis with Internal Encodings
No full textWhite-box cryptography aims at protecting implementations of cryptographic algorithms against a very powerful attacker who controls the execution environment. The first defensive brick traditionally embedded in such implementations consists of encodings, which are bijections supposed to conceal sensitive data manipulated by the white-box. Several previous works have sought to evaluate the relevance of encodings to protect white-box implementations against grey-box attacks such as Differential Computation Analysis (DCA). However, these works have been either probabilistic or partial in nature. In particular, while they showed that DCA succeeds with high probability against AES white-box implementations protected by random encodings, they did not refute the existence of a particular class of encodings that could prevent the attack. One could thus wonder if carefully crafting specific encodings instead of drawing random bijections could be a solution.This article bridges the gap between preceding research efforts and investigates this question. We first focus on the protection of the S-box output and we show that no 4-bit encoding can actually protect this sensitive value against side-channel attacks. We then argue that the use of random 8-bit encodings is both necessary and sufficient, but that this assertion holds exclusively for the S-box output. Indeed, while we define a class of 8-bit encodings that actually prevents a classical DCA targeting the MixColumns output, we also explain how to adapt this attack and exploit the correlation traces in order to defeat even these specific encodings. Our work thus rules out the existence of a set of practical encodings that could be used to protect an AES white-box implementation against DCA-like attacks
Bake It Till You Make It: Heat-induced Power Leakage from Masked Neural Networks
No full textMasking has become one of the most effective approaches for securing hardware designs against side-channel attacks. Regardless of the effort put into correctly implementing masking schemes on a field-programmable gate array (FPGA), leakage can be unexpectedly observed. This is due to the fact that the assumption underlying all masked designs, i.e., the leakages of different shares are independent of each other, may no longer hold in practice. In this regard, extreme temperatures have been shown to be an important factor in inducing leakage, even in correctlymasked designs. This has previously been verified using an external heat generator (i.e., a climate chamber). In this paper, we examine whether the leakage can be induced using the circuit components themselves without making any changes to the design. Specifically, we target masked neural networks (NNs) in FPGAs, one of the main building blocks of which is block random access memory (BRAM). In this respect, thanks to the inherent characteristics of NNs, our novel internal heat generators leverage solely the memories devoted to storing the user’s input, especially when frequently writing alternating patterns into BRAMs. The possibility of observing first-order leakage is evaluated by considering one of the most recent and successful first-order secure masked NNs, namely ModuloNET. ModuloNET is specifically designed for FPGAs, where BRAMs are used to store inputs and intermediate computations. Our experimental results demonstrate that undesirable first-order leakage can be observed and exploited by increasing the temperature when an alternating input is applied to the masked NN. To give a better understanding of the impact of extreme heat, we further perform a similar test on the design using an external heat generator, where a similar conclusion can be drawn
