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Item does not contain fulltext We explore how data modification can enhance privacy by examining the connection between data modification and machine learning. Specifically, machine learning “meets” data modification in two ways. First, data modification can protect the data that is used to train machine learning models focusing it on the intended use and inhibiting unwanted inference. Second, machine learning can provide new ways of creating modified data. In this chapter, we discuss data modification approaches, applied during data pre-processing, that are suited for online data sharing scenarios. Specifically, we define two scenarios “User data sharing” and “Data set sharing” and describe the threat models associated with each scenario and related privacy threats. We then survey the landscape of privacy-enhancing data modification techniques that can be used to counter these threats. The picture that emerges is that data modification approaches hold promise to enhance privacy, and can be used alongside of conventional cryptographic approaches. We close with an outlook on future directions focusing on new types of data, the relationship among privacy, and the importance of taking an interdisciplinary approach to data modification for privacy enhancement.

We explore how data modification can enhance privacy by examining the connection between data modification and machine learning. Specifically, machine learning “meets” data modification in two ways. First, data modification can protect the data that is used to train machine learning models focusing it on the intended use and inhibiting unwanted inference. Second, machine learning can provide new ways of creating modified data. In this chapter, we discuss data modification approaches, applied during data pre-processing, that are suited for online data sharing scenarios. Specifically, we define two scenarios “User data sharing” and “Data set sharing” and describe the threat models associated with each scenario and related privacy threats. We then survey the landscape of privacy-enhancing data modification techniques that can be used to counter these threats. The picture that emerges is that data modification approaches hold promise to enhance privacy, and can be used alongside of conventional cryptographic approaches. We close with an outlook on future directions focusing on new types of data, the relationship among privacy, and the importance of taking an interdisciplinary approach to data modification for privacy enhancement.

Item does not contain fulltext IX, 161 p.

Contains fulltext : 195384.pdf (Publisher’s version ) (Open Access) We present KangarooTwelve, a fast and secure arbitrary output-length hash function aiming at a higher speed than the FIPS 202’s SHA-3 and SHAKE functions. While sharing many features with SHAKE128, like the cryptographic primitive, the sponge construction, the eXtendable Output Function (XOF) and the 128-bit security strength, KangarooTwelve offers two major improvements over its standard counterpart. First it has a built-in parallel mode that efficiently exploits multi-core or SIMD instruction parallelism for long messages, without impacting the performance for short messages. Second, relying on the cryptanalysis results on Keccak over the past ten years, we tuned its permutation to require twice less computation effort while still offering a comfortable safety margin. By combining these two changes KangarooTwelve consumes less than 0.55 cycles/byte for long messages on the latest Intel $$^{\circledR }$$ ® ’s SkylakeX architectures. The generic security of KangarooTwelve is guaranteed by the use of Sakura encoding for the tree hashing and of the sponge construction for the compression function.

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Education in general and cybersecurity education in particular can be made more attractive by adding hands-on experience to classrooms. This requires new technology, such as virtualisation, to be developed fully geared towards the needs of educational purposes. Over the years, several techniques have been developed by the authors. In this paper, the authors first give a full account of their earlier work on a distributed virtual computer lab for cybersecurity education. Then, this virtual lab is extended with educational enhancements, such as an intelligent tutoring system, which resulted in a prototype for a virtual classroom for cybersecurity education.

Model learning is a black-box technique for constructing state machine models of software and hardware components, which has been successfully used in areas such as telecommunication, banking cards, network protocols, and control software. The underlying theoretic framework (active automata learning) was first introduced in a landmark paper by Dana Angluin in 1987 for finite state machines. In order to make model learning more widely applicable, it must be further developed to scale better to large models and to generate richer classes of models. Recently, various techniques have been employed to extend automata learning to extended automata models, which combine control flow with guards and assignments to data variables. Such techniques infer guards over data parameters and assignments from observations of test output. In the black-box model of active automata learning this can be costly and require many tests, while in many application scenarios source code is available for analysis. In this paper, we explore some directions for future research on how black-box model learning can be enhanced using white-box information extraction methods, with the aim to maintain the benefits of dynamic black-box methods while making effective use of information that can be obtained through white-box techniques.

We describe a large collection of benchmarks, publicly available through the wiki automata.cs.ru.nl, of different types of state machine models: DFAs, Moore machines, Mealy machines, interface automata and register automata. Our repository includes both randomly generated state machines and models of real protocols and embedded software/hardware systems. These benchmarks will allow researchers to evaluate the performance of new algorithms and tools for active automata learning and conformance testing.

This work introduces XMSS-T, a new stateful hash-based signature scheme with tight security. Previous hash-based signatures are facing a loss of security, linear in performance parameters such as the total tree height. Our new scheme can achieve the same security level but using hash functions with a smaller output length, which immediately leads to a smaller signature size. The same techniques also apply directly to the recent stateless hash-based signature scheme SPHINCS Eurocrypt 2015, and the signature size is reduced as well. Being a little more specific and technical, the tight security stems from new multi-target notions of hash-function properties which we define and analyze. We show precise complexity for breaking these security properties under both classical and quantum generic attacks, thus establishing a reliable estimate for the quantum security of XMSS-T. Especially, we prove quantum query complexity tailored for cryptographic applications, which overcome some limitations of standard techniques in quantum query complexity such as they usually only consider worst-case complexity. Our proof techniques may be useful elsewhere. We also implement XMSS-T and compare its performance to that of XMSS PQCrypto 2011, the most recent stateful hash-based signature scheme before our work.

Contains fulltext : 162562.pdf (Publisher’s version ) (Closed access) 12 september 2016

Item does not contain fulltext 605 p.

International audience; The cost of higher-order masking as a countermeasure against side-channel attacks is often considered too high for practical scenarios, as protected implementations become very slow. At Eurocrypt 2017, the bounded moment leakage model was proposed to study the (theoretical) security of parallel implementations of masking schemes [5]. Work at CHES 2017 then brought this to practice by considering an implementation of AES with 32 shares [26], bitsliced inside 32-bit registers of ARM Cortex-M processors. In this paper we show how the NEON vector instructions of larger ARM Cortex-A processors can be exploited to build much faster masked implementations of AES. Specifically, we present AES with 4 and 8 shares, which in theory provide security against 3rd and 7th-order attacks, respectively. The software is publicly available and optimized for the ARM Cortex-A8. We use refreshing and multiplication algorithms that are proven to be secure in the bounded moment leakage model and to be strongly non-interfering. Additionally, we perform a concrete side-channel evaluation on a BeagleBone Black, using a combination of test vector leakage assessment (TVLA), leakage certification tools and information-theoretic bounds.

Contains fulltext : 197662.pdf (Publisher’s version ) (Open Access)

System designers need to have insight in the response times of service systems to see if they meet performance requirements. We present a high-level evaluation technique to obtain the distribution of services completion times. It is based on a high-level domain-specific language that hides the underlying technicalities from the system designer. Under the hood, probabilistic real-time model checking technology is used iteratively to obtain precise bounds and probabilities. This allows reasoning about nondeterministic, probabilistic and real-time aspects in a single evaluation. To reduce the state spaces for analysis, we use two sampling methods (for measurements) that simplify the system model: (i) applying an abstraction on time by increasing the length of a (discrete) model time unit, and (ii) computing only absolute bounds by replacing probabilistic choices with non-deterministic ones. We use an industrial case on image processing of an interventional X-ray system to illustrate our approach.

Self-blindable credential schemes allow users to anonymously prove ownership of credentials. This is achieved by randomizing the credential before each showing in such a way that it still remains valid. As a result, each time a different version of the same credential is presented. A number of such schemes have been proposed, but unfortunately many of them are broken, in the sense that they are linkable (i.e., failing to protect the privacy of the user), or malleable (i.e., they allow users to create new credentials using one or more valid credentials given to them). In this paper we prove a general theorem that relates linkability and malleability in self-blindable credential schemes, and that can test whether a scheme is linkable or malleable. After that we apply the theorem to a number of self-blindable credential schemes to show that they suffer from one or both of these issues.

We answer an open question in the theory of degrees of infinite sequences with respect to transducibilityby finite-state transducers. An initial study of this partial order of degrees was carried out in [1], but many basic questions remain unanswered.One of the central questions concerns the existence of atom degrees, other than the degree of the ‘identity sequence’ 100101102103⋯ . A degree is called an ‘atom’ if below it there is only the bottom degree 00 , which consists of the ultimately periodic sequences. We show that also the degree of the ‘squares sequence’ 1001011041091016⋯ is an atom.As the main tool for this result we characterise the transducts of ‘spiralling’ sequences and their degrees. We use this to show that every transduct of a ‘polynomial sequence’ either is in 00 or can be transduced back to a polynomial sequence for a polynomial of the same order.