Concrete Instantiation Research Articles

Block Cipher Doubling for a Post-Quantum World

In order to maintain a similar security level in a post-quantum setting, many symmetric primitives should have to double their keys and increase their state sizes. So far, no generic way for doing this is known that would provide convincing quantum security guarantees. In this paper we propose a new generic construction, QuEME, that allows one to double the key and the state size of a block cipher in such a way that a decent level of quantum security is guaranteed. The QuEME design is inspired by the ECB-Mix-ECB (EME) construction, but is defined for a different choice of mixing function than what we have seen before, in order to withstand a new quantum superposition attack that we introduce as a side result: this quantum superposition attack exhibits a periodic property found in collisions and breaks EME and a large class of its variants. We prove that QuEME achieves n-bit security in the classical setting, where n is the block size of the underlying block cipher, and at least (n/6)-bit security in the quantum setting. We finally propose a concrete instantiation of this construction, called Double-AES, that is built with variants of the standardized AES-128 block cipher.

Denoising and Augmented Negative Sampling for Collaborative Filtering

Negative sampling plays a crucial role in implicit-feedback-based collaborative filtering, where it leverages massive unlabeled data to generate negative signals for guiding supervised learning. The current state-of-the-art approaches focus on utilizing hard negative samples that contain more information to establish a better decision boundary. To strike a balance between efficiency and effectiveness, most existing methods adopt a two-pass approach: in the first pass, a fixed number of unobserved items are sampled using a simple static distribution, while, in the second pass, a more sophisticated negative sampling strategy is employed to select the final negative items. However, selecting negative samples solely from the original items in a dataset is inherently restricted due to the limited available choices, and thus may not be able to effectively contrast positive samples. In this paper, we empirically validate this observation through meticulously designed experiments and identify three major limitations of existing solutions: ambiguous trap, information discrimination, and false negative samples. Our response to such limitations is to introduce “denoised” and “augmented” negative samples that may not exist in the original dataset. This direction renders a few substantial technical challenges. First, constructing augmented negative samples may introduce excessive noise that eventually distorts the decision boundary. Second, the scarcity of supervision signals hampers the denoising process. To this end, we introduce a novel generic denoising and augmented negative sampling (DANS) paradigm and provide a concrete instantiation. First, we disentangle the hard and easy factors of negative items. Then, we regulate the augmentation of easy factors by carefully considering the direction and magnitude. Next, we propose a reverse attention mechanism to learn a user’s negative preference, which allows us to perform a dimension-level denoising procedure on hard factors. Finally, we design an advanced negative sampling strategy to identify the final negative samples, taking into account both the score function used in existing methods and a novel metric called synthesization gain. Through extensive experiments on real-world datasets, we demonstrate that our method substantially outperforms state-of-the-art baselines. Our code is publicly available at https://github.com/Asa9aoTK/ANS-Recbole.

Improved unbounded inner-product functional encryption

In this paper, we propose an unbounded inner-product functional encryption (unbounded IPFE) scheme with semi-adaptive simulation-based security. Compared with the previous semi-adaptive secure scheme proposed by Tomida and Takashima [Asiacrypt18], our scheme enjoys about 28% shorter ciphertext and about 43% shorter secret key.Technically, we start with a bounded separable one-key IPFE scheme. In the separable one-key IPFE scheme, the public key and ciphertext can be divided into some vectors. At the same time, we develop a new transformation from a bounded separable one-key IPFE scheme towards an unbounded IPFE scheme. Finally, we give a concrete instantiation with the bounded separable one-key IPFE scheme and the transformation.

Unfolding the entangled geosemiotics of technologically-mediated gated communities: Mountain View’s (2021) advertisement as a case study

Gated Communities (GCs), as residential enclaves in urban and suburban environments that offer exclusive housing and a multitude of onsite amenities, are burgeoning in many fast-growing Egyptian cities. In the past few years, several upscale Egyptian GCs have received unequivocal attention in high-profile TV advertisements and campaigns which package and technologically mediate social spaces overlaid with aesthetics of unique architectural character, distinctive leisure amenities, social exclusiveness, and prestigious lifestyles. Set against this backdrop, this paper takes up a postphenomenological-geosemiotic approach toward the examination of the affects of elitism that transpire in one upmarket urban enclave’s advertisement, namely Mountain View. Arguably, in the filmic narrative of the promoted social spaces, the technological mediation and intentionality of the camera shot scale modulate multi-sensory and multi-spatio-temporal relationships between viewers and the GC lifeworld/residents that are worthy of scrutiny. Key findings include how POV camera shots are employed and appropriated to mitigate (and mediate) the representation of transnational elite semiotic landscapes and the pertinent affective intersubjectivities of doing eliteness. The systematic analysis of MV’s semiotic aggregate unravels an affective aggregate that is, in essence, a concrete instantiation of Foucault’s notions of heterotopia and (post)-panopticism.

Learning decision catalogues for situated decision making: The case of scoring systems

In this paper, we formalize the problem of learning coherent collections of decision models, which we call decision catalogues, and illustrate it for the case where models are scoring systems. This problem is motivated by the recent rise of algorithmic decision-making and the idea to improve human decision-making through machine learning, in conjunction with the observation that decision models should be situated in terms of their complexity and resource requirements: Instead of constructing a single decision model and using this model in all cases, different models might be appropriate depending on the decision context. Decision catalogues are supposed to support a seamless transition from very simple, resource-efficient to more sophisticated but also more demanding models. We present a general algorithmic framework for inducing such catalogues from training data, which tackles the learning task as a problem of searching the space of candidate catalogues systematically and, to this end, makes use of heuristic search methods. We also present a concrete instantiation of this framework as well as empirical studies for performance evaluation, which, in a nutshell, show that greedy search is an efficient and hard-to-beat strategy for the construction of catalogues of scoring systems.

Adaptive Hardness Negative Sampling for Collaborative Filtering

Negative sampling is essential for implicit collaborative filtering to provide proper negative training signals so as to achieve desirable performance. We experimentally unveil a common limitation of all existing negative sampling methods that they can only select negative samples of a fixed hardness level, leading to the false positive problem (FPP) and false negative problem (FNP). We then propose a new paradigm called adaptive hardness negative sampling (AHNS) and discuss its three key criteria. By adaptively selecting negative samples with appropriate hardnesses during the training process, AHNS can well mitigate the impacts of FPP and FNP. Next, we present a concrete instantiation of AHNS called AHNS_{p

XDRBG: A Proposed Deterministic Random Bit Generator Based on Any XOF

A deterministic random bit generator (DRBG) generates pseudorandom bits from an unpredictable seed, i.e., a seed drawn from any random source with sufficient entropy. The current paper formalizes a security notion for a DRBG, in which an attacker may make any legal sequence of requests to the DRBG and sometimes compromise the DRBG state, but should still not be able to distingush DRBG outputs from ideal random bits. The paper proposes XDRBG, a new DRBG based on any eXtendable Output Function (XOF) and proves the security of the XDRBG in the ideal-XOF model. The proven bounds are tight, as demonstrated by matching attacks. The paper also discusses the security of XDRBG against quantum attackers. Finally, the paper proposes concrete instantiations of XDRBG, employing either the SHAKE128 or the SHAKE256 XDRBG. Alternative instantiations suitable for lightweight applications can be based on ASCON.

Design and Implementation of Static Analyses for Tezos Smart Contracts

Once deployed in blockchain, smart contracts become immutable: attackers can exploit bugs and vulnerabilities in their code, that cannot be replaced with a bug-free version. For this reason, the verification of smart contracts before they are deployed in blockchain is important. However, the development of verification tools is not easy, especially if one wants to obtain guarantees by using formal methods. This paper describes the development, from scratch, of a static analyzer based on abstract interpretation for the verification of real-world Tezos smart contracts. The analyzer is generic with respect to the property under analysis. This paper shows taint analysis as a concrete instantiation of the analyzer, at different levels of precision, to detect untrusted cross-contract invocations.

Value theories in motion: Circular labour migration, unfinished land dispossession and reproductive struggles across the urban–rural divide

This analysis theorises the central role of the urban–rural divide in the making of value relations and exploitation in contemporary labour regimes. Inspired by insights contained in Diane Elson’s ‘value theory of labour’ and informed by evidence on labour circulation in India’s ‘Sweatshop Regime’, the article combines Early Social Reproduction Analyses (ESRA) and debates on ‘forms of exploitation’ to illustrate the integrated nature of the circuits incorporating production and reproduction, use and exchange value across the urban–rural divide. It represents these circuits as a concrete instantiation of ‘value in motion’. In this schema, the countryside emerges as central to the regeneration of the urban labour regime; as key provider of labouring bodies; and as absorber of reproductive costs, also performing the function of ‘global housework’ for contemporary capitalism. The narrative is particularly attentive to post-industrial work trajectories, which further explain how partial land dispossession and informal work interplay to sustain the dynamic nature of value relations as well as workers’ livelihoods beyond factory labour. The conclusions stress the political implications of reproductive readings of value for labour struggles and pro-labour policy.

Predicate encryption with selective-opening security for receivers: formal definition, generic construction, and concrete instantiations for several primitives

Predicate encryption with selective-opening security for receivers: formal definition, generic construction, and concrete instantiations for several primitives

Certificate-based authenticated encryption with keyword search: Enhanced security model and a concrete construction for Internet of Things

The Internet of Things (IoT) devices produce a humongous amount of data frequently stored on cloud servers, and as a result, cryptographic techniques to guarantee the privacy of outsourced data while preserving search ability on servers are becoming indispensable research topics. A prominent example of such topics is the concept of public key authenticated encryption with keyword search (PAEKS). In spite of the large number of PAEKS schemes in the literature, most existing schemes exhibit issues in certificate management, key escrow, or even key distribution. To address these issues, recently, PAEKS schemes in the certificate-based setting have gained attention. To the best of our knowledge, there exist only two Certificate-Based Authenticated Encryption with Keyword Search (CBAEKS) schemes, both presented with rather weak security models based on single keyword challenges. In this paper, we propose an enhanced security model for CBAEKS which captures notions of multi-ciphertext and multi-trapdoor indistinguishability, then proceed to devise a concrete instantiation for a CBAEKS scheme and formally prove its security under our enhanced model. Furthermore, we prove that the existing CBAEKS schemes are insecure under the enhanced security model. Comparisons with related schemes in the literature are also provided to demonstrate that the enhanced security is achieved at some affordable costs.

To pass or not to pass: Privacy-preserving physical access control

Anonymous or attribute-based credential (ABC) systems are a versatile and important cryptographic tool to achieve strong access control guarantees while simultaneously respecting the privacy of individuals. A major problem in the practical adoption of ABCs is their transferability, i.e., such credentials can easily be duplicated, shared or lent. One way to counter this problem is to tie ABCs to biometric features of the credential holder and to require biometric verification on every use. While this is certainly not a viable solution for all ABC use-cases, there are relevant and timely use-cases, such as vaccination credentials as widely deployed during the COVID-19 pandemic. In such settings, ABCs that are tied to biometrics, which we call Biometric-Bound Attribute-Based Credentials (bb-ABC ), allow to implement scalable and privacy-friendly systems to control physical access to (critical) infrastructure and facilities.While there are some previous works on bb-ABC in the literature, the state of affairs is not satisfactory. Firstly, in existing work the problem is treated in a very abstract way when it comes to the actual type of biometrics. Thus, it does not provide concrete solutions which allow for assessing their practicality when deployed in a real-world setting. Secondly, there is no formal model which rigorously captures bb-ABC systems and their security requirements, making it hard to assess their security guarantees. With this work we overcome these limitations and provide a rigorous formalization of bb-ABC systems. Moreover, we introduce two generic constructions which offer different trade-offs between efficiency and trust assumptions, and provide benchmarks from a concrete instantiation of such a system using facial biometrics. The latter represents a contact-less biometric feature that provides acceptable accuracy and seems particularly suitable to the above use-case.

Chosen-ciphertext secure code-based threshold public key encryptions with short ciphertext

Threshold public-key encryption (threshold PKE) has various useful applications. A lot of threshold PKE schemes are proposed based on RSA, Diffie–Hellman and lattice, but to the best of our knowledge, code-based threshold PKEs have not been proposed. In this paper, we provide three IND-CCA secure code-based threshold PKE schemes. The first scheme is the concrete instantiation of Dodis–Katz conversion (Dodis and Katz, TCC’05) that converts an IND-CCA secure PKE into an IND-CCA secure threshold PKE using parallel encryption and a signature scheme. This approach provides non-interactive threshold decryption, but ciphertexts are large (about 16 kilobytes for 128-bit security) due to long code-based signatures even in the state-of-the-art one. The second scheme is a new parallel encryption-based construction without signature schemes. Unlike the Dodis–Katz conversion, our parallel encryption converts an OW-CPA secure PKE into an OW-CPA secure threshold PKE. To enhance security, we use Cong et al.’s conversion (Cong et al., ASIACRYPT’21). Thanks to eliminating signatures, its ciphertext is 512 bytes, which is only 3% of the first scheme. The decryption process needs an MPC for computing hash functions, but decryption of OW-CPA secure PKE can be done locally. The third scheme is an MPC-based threshold PKE scheme from code-based assumption. We take the same approach Cong et al. took to construct efficient lattice-based threshold PKEs. We build an MPC for the decryption algorithm of OW-CPA secure Classic McEliece PKE. This scheme has the shortest ciphertext among the three schemes at just 192 bytes. Compared to the regular CCA secure Classic McEliece PKE, the additional ciphertext length is only 100 bytes. The cons are heavy distributed computation in the decryption process.

A privacy-preserving attribute-based framework for IoT identity lifecycle management

The Internet of Things (IoT) has brought a new era of interconnected devices and seamless data exchange. As the IoT ecosystem continues to expand, there is an increasing need for effective identity management mechanisms, specifically for authorization processes and access control. The pervasiveness of such devices demands that desirable solutions tackle not only security properties but also privacy aspects like granular control over which identity data is shared in authentication/authorization processes, covering aspects like bootstrapping, enrolment, and service provision. In this context, it is natural to turn to privacy-enhancing technologies, like (privacy-preserving) Attribute-Based Credentials (p-ABC), for achieving both high security and privacy guarantees. Nonetheless, these technical tools need to be accompanied by a comprehensive approach that deals with the particularities of IoT scenarios and covers the full lifetime of the device. In this work, we propose the use of a p-ABC scheme with support for distributed issuance (dp-ABC) as a keystone for privacy-preserving attribute-based authentication and authorization in IoT scenarios. We integrate said cryptographic scheme with W3C’s Verifiable Credentials standard, evaluating its impact to gauge its feasibility. The integration facilitates adoption and, particularly, allows the solution to transparently coexist with simpler techniques in heterogeneous scenarios that demand them. Moreover, we define and analyse a generic and comprehensive framework for identity management that identifies challenges throughout the device’s lifetime to achieve IoT privacy-preserving identity management following self-sovereign principles. We show how the various aspects identified in the framework are tackled in a concrete instantiation as part of the H2020 project ERATOSTHENES.

Policy-Based Chameleon Hash with Black-Box Traceability for Redactable Blockchain in IoT

Blockchain has become an integral part of various IoT applications, and it has been successful in boosting performance in various aspects. Applying blockchain as a trust solution for Internet-of-Things is a viable approach. The immutability of blockchain is essential to prevent anyone from manipulating registered IoT data transactions for illegitimate benefits. However, the increasing abuse of blockchain storage negatively impacts the development of IoT blockchain and potential stakeholders are discouraged from joining the IoT data sharing as the IoT data recorded on the blockchain contains private information. Hence, it is crucial to find ways to redact data stored on the IoT blockchain, which is also supported by relevant laws and regulations. Policy-based chameleon hash is useful primitive for blockchain rewriting, allowing the modifier to rewrite the transaction if they possess enough rewriting privileges that satisfy the access policy. However, this approach lacks traceability, which can be exploited by malicious modifiers to grant unauthorized user modification privileges for personal gain. To overcome this deficiency, we introduce a new design of policy-based chameleon hash with black-box traceability (PCHT), which enables the authority to identify the set of producers responsible for generating the pirate decoder. Specifically, PCHT is constructed by practical attribute-based encryption with black-box traceability (ABET) and collision-resistant chameleon hash with ephemeral trapdoor (CHET). After modeling PCHT, we present its concrete instantiation and rigorous security proofs. Finally, a PCHT-based redactable transaction scheme for IoT blockchain is given. Compared to the state-of-the-art mutable blockchain solutions, our scheme provides fine-grained blockchain rewriting and black-box traceability. The evaluation results demonstrate that our scheme is efficient and practical while still ensuring that no computational overhead is placed on IoT devices with limited computing resources.

Online continual learning with declarative memory

Deep neural networks are enjoying unprecedented attention and success in recent years. However, catastrophic forgetting undermines the performance of deep models when the training data are arrived sequentially in an online multi-task learning fashion. To address this issue, we propose a novel method named continual learning with declarative memory (CLDM) in this paper. Specifically, our idea is inspired by the structure of human memory. Declarative memory is a major component of long-term memory which helps human beings memorize past experiences and facts. In this paper, we propose to formulate declarative memory as task memory and instance memory in neural networks to overcome catastrophic forgetting. Intuitively, the instance memory recalls the input–output relations (fact) in previous tasks, which is implemented by jointly rehearsing previous samples and learning current tasks as replaying-based methods act. In addition, the task memory aims to capture long-term task correlation information across task sequences to regularize the learning of the current task, thus preserving task-specific weight realizations (experience) in high task-specific layers. In this work, we implement a concrete instantiation of the proposed task memory by leveraging a recurrent unit. Extensive experiments on seven continual learning benchmarks verify that our proposed method is able to outperform previous approaches with tremendous improvements by retaining the information of both samples and tasks.

Finding Collisions for Round-Reduced Romulus-H

The hash function Romulus-H is a finalist in the NIST Lightweight Cryptography competition. It is based on the Hirose double block-length (DBL) construction which is provably secure when used with an ideal block cipher. However, in practice, ideal block ciphers can only be approximated. Therefore, the security of concrete instantiations must be cryptanalyzed carefully; the security margin may be higher or lower than in the secret-key setting. So far, the Hirose DBL construction has been studied with only a few other block ciphers, like IDEA and AES. However, Romulus-H uses Hirose DBL with the SKINNY block cipher where only very little analysis has been published so far. In this work, we present the first practical analysis of Romulus-H. We propose a new framework for finding collisions in hash functions based on the Hirose DBL construction. This is in contrast to previous work that only focused on free-start collisions. Our framework is based on the idea of joint differential characteristics which capture the relationship between the two block cipher calls in the Hirose DBL construction. To identify good joint differential characteristics, we propose a combination of MILP and CP models. Then, we use these characteristics in another CP model to find collisions. Finally, we apply this framework to Romulus-H and find practical collisions of the hash function for 10 out of 40 rounds and practical semi-free-start collisions for up to 14 rounds.

ARMOR: Differential Model Distribution for Adversarially Robust Federated Learning

In this work, we formalize the concept of differential model robustness (DMR), a new property for ensuring model security in federated learning (FL) systems. For most conventional FL frameworks, all clients receive the same global model. If there exists a Byzantine client who maliciously generates adversarial samples against the global model, the attack will be immediately transferred to all other benign clients. To address the attack transferability concern and improve the DMR of FL systems, we propose the notion of differential model distribution (DMD) where the server distributes different models to different clients. As a concrete instantiation of DMD, we propose the ARMOR framework utilizing differential adversarial training to prevent a corrupted client from launching white-box adversarial attack against other clients, for the local model received by the corrupted client is different from that of benign clients. Through extensive experiments, we demonstrate that ARMOR can significantly reduce both the attack success rate (ASR) and average adversarial transfer rate (AATR) across different FL settings. For instance, for a 35-client FL system, the ASR and AATR can be reduced by as much as 85% and 80% over the MNIST dataset.

User Cognition Antecedents of Smart Assistant Systems in Cars

By customizing smart assistance systems to customer needs, car manufacturers can improve their systems and create additional benefits for users. However, it is still unclear which characteristics car drivers perceive as favorable and useful. To examine this, we employ a mixed-method approach. In our first study, we conduct a survey (N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$ </tex-math></inline-formula> 301) to investigate general user perception antecedents of smart assistant systems in cars. We analyze the indirect effects of different system quality characteristics mediated by user perception on their usage intention. In our second study (N <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$=$ </tex-math></inline-formula> 270), we use a discrete choice experiment to measure the effects of concrete system attributes on user acceptance of IT-based parking systems representing a concrete instantiation of a smart assistance system. Consistent with our first study, we observe that the system quality factors user interface intuitiveness, full language flexibility, and system error occurrence significantly influence the consumers’ intention to use the technology. Accordingly, car manufacturers should put a particular focus on these factors when developing and implementing their smart assistance systems in cars. For IT-based parking systems, in particular, consumers are very price-sensitive. However, by implementing additional technical features, manufacturers can significantly increase the systems’ price value and thus the purchase probability.

Achieving fuzzy matching data sharing for secure cloud-edge communication

In this paper, we propose a novel fuzzy matching data sharing scheme named FADS for cloud-edge communications. FADS allows users to specify their access policies, and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies simultaneously. Specifically, we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge environments. Then, we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the policies. If the matching succeeds, the data can be decrypted. Otherwise, nothing will be revealed. In addition, FADS allows users to dynamically specify the policy for each time, which is an urgent demand in practice. A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack (IND-CCA) in random oracle model against probabilistic polynomial-time (PPT) adversary, and the desirable security properties of privacy and authenticity are achieved. Extensive experiments provide evidence that FADS is with acceptable efficiency.