Asymptotically Compact Adaptively Secure Lattice IBEs and Verifiable Random Functions via Generalized Partitioning Techniques

Cohen, Goldwasser, and Vaikuntanathan (TCC’15) introduced the concept of aggregate pseudo-random functions (PRFs), which allow efficiently computing the aggregate of PRF values over exponential-sized sets. In this paper, we explore the aggregation augmentation on verifiable random function (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), as well as its application to e-lottery schemes. We introduce the notion of static aggregate verifiable random functions (Agg-VRFs), which perform aggregation for VRFs in a static setting. Our contributions can be summarized as follows: (1) we define static aggregate VRFs, which allow the efficient aggregation of VRF values and the corresponding proofs over super-polynomially large sets; (2) we present a static Agg-VRF construction over bit-fixing sets with respect to product aggregation based on the q-decisional Diffie–Hellman exponent assumption; (3) we test the performance of our static Agg-VRFs instantiation in comparison to a standard (non-aggregate) VRF in terms of costing time for the aggregation and verification processes, which shows that Agg-VRFs lower considerably the timing of verification of big sets; and (4) by employing Agg-VRFs, we propose an improved e-lottery scheme based on the framework of Chow et al.’s VRF-based e-lottery proposal (ICCSA’05). We evaluate the performance of Chow et al.’s e-lottery scheme and our improved scheme, and the latter shows a significant improvement in the efficiency of generating the winning number and the player verification.

Randomness is a critical issue in peer-to-peer networks because random numbers allow us to fairly select the candidates, resolve the lotteries, select block producers, etc. The difficulty is that each participant wants to verify that the random number was randomly generated, this led to the invention of verifiable random functions (VRF). The VRF is a pseudo-random function that provides a solution to blockchain-based random number generation. This paper focuses on the implementation of an elliptic curve-based VRF introduced by NIST called ECVRF-secp256r1 in Solidity. The algorithm verifies the randomness on-chain, tests the gas consumption at each stage, and compares it to the ECVRF-secp256k1.

We give a simple and efficient construction of a verifiable random function (VRF) on bilinear groups. Our construction is direct. In contrast to prior VRF constructions [14,15], it avoids using an inefficient Goldreich-Levin transformation, thereby saving several factors in security. Our proofs of security are based on a decisional bilinear Diffie-Hellman inversion assumption, which seems reasonable given current state of knowledge. For small message spaces, our VRF’s proofs and keys have constant size. By utilizing a collision-resistant hash function, our VRF can also be used with arbitrary message spaces. We show that our scheme can be instantiated with an elliptic group of very reasonable size. Furthermore, it can be made distributed and proactive.

This study has proposed and investigated a novel input variable selection method for nonlinear modelling based on principle component analysis (PCA) and cluster analysis. The proposed approach was applied to daily rainfall-runoff modelling of the Brue catchment of the United Kingdom using wavelet based hybrid forms of two nonlinear models, Artificial Neural Networks (ANNs) and Local Linear Regression (LLR), to identify meaningful wavelet decomposed sub-series. The homogenous group formation capability of cluster analysis and redundancy assessment capability of PCA were applied effectively in this study to solve input selection uncertainties associated with wavelet based hybrid models. Though this concept has been represented in the selection of effective wavelet decomposed subseries in runoff modelling, the application has gotten wider implications in time series modelling with highly redundant and large input space. The study revealed the weakness of conventional forms of cross-correlation analysis and also suggested that input selection could be improved by making sufficient natural clusters (equal to the desired number of input data series) of input space and restricting the search within each cluster according to silhouette or correlation value. The study also highlighted the higher modelling capability of ANN over traditional LLR models in rainfall-runoff modelling of the Brue catchment.

Effective poultry management practices that promote chicken health are crucial for producing higher-quality chicken meat at a lower cost. This study examined the hypothesis that increasing space while maintaining stocking density may positively impact poultry health. We evaluated body weight (BW) as an indicator of growth, stress markers, and the composition of the gut microbiome by comparing two housing sizes: smaller (control) and larger (treatment) spaces, with 10 birds per space and a stocking density of 12.3 birds/m2. Chickens in the larger space had 15% higher BW (p = 0.06) compared to those in the smaller space when significance was evaluated at p < 0.10. Stress indicators such as blood cortisol (acute) and brain FKBP51 did not differ significantly. Faith's phylogenetic diversity was marginally higher in the larger space (p = 0.05), and microbial communities differed significantly between the two groups. The relative abundance of several genera, including Clostridium_sensu_stricto_1 (p = 0.02), Lactobacillus (p = 0.03), and Paracoccus (p < 0.01), was greater in the larger space, whereas Turicibacter (p = 0.02), Escherichia-Shigella (p = 0.01), and Lysinibacillus (p = 0.01) were more abundant in the smaller space. The larger and smaller spaces were associated with a significant (p < 0.05) increase in the abundance of 39 and 25 MetaCyc pathways, respectively, involved in amino acid and nitrogen metabolisms. These findings suggest that increasing housing space without altering stocking density or additional treatments may improve both growth and gut microbiome health in broilers. Our results provide insights into the relationship between chicken housing environments and the gut microbiome.

The attribute-based encryption (ABE) scheme with monotonic access structure cannot deal with an access structure that is associated with the negation of attributes, which is not convenient for real world applications. In this paper, we attempt to propose a more expressive non-monotonic ABE scheme through a new method. To achieve this goal, we firstly propose a linear two-mode identity-based broadcast encryption (TIBBE) scheme based on an identity-based broadcast encryption (IBBE) scheme. We introduce the concept of identity-based revocation (IBR) for this scheme without increasing the size of parameters in IBBE. The scheme is selective identity secure under the m-DBDHE assumption. Then, we convert the TIBBE scheme into a non-monotonic key-policy attribute-based encryption (KP-ABE) scheme with compact parameters. Our KP-ABE scheme could achieve constant-size ciphertexts, and the scale of the private keys grows linearly with the scale of the attribute set. Besides, the computational cost is the lowest compared with other existing non-monotonic KP-ABE schemes.

A challenging task in time series microarray data analysis is to identify co-expressed groups of genes from a large input space. The overall objective of this study is to obtain knowledge about the most important genes and clusters related to production and growth rate in a real-world microarray data analysis task. Various measures are engaged to evaluate the importance of each gene and to group genes based on their correlation with the output and each other. Some strategies for grouping and selecting genes are integrated resulting in several models tested for real biological data. All proposed models are tested on a real microarray data analysis problem and the results obtained are throughtly presented as well as interpreted from a biological perspective.

A Graphical User Interface (GUI) consists of virtual objects (widgets) that are more intuitive to use, for example buttons, edit boxes, etc. While GUIs have become ubiquitous, testing them remains largely ad- hoc. In contrast with console applications where there is only one point of interaction (the command line), GUIs provide multiple points each of which might have different states. This structure makes GUI testing especially challenging because of its large input space.

The construction of a neural network simulation metamodel requires the generation of training data; design points (inputs) and the estimate of the corresponding output generated by the simulation model. A common methodology is to focus some simulation effort in obtaining accurate estimates of the expected output values by executing several simulation replications at each point and taking the average as the estimate. However, with a limited amount of simulation effort available and a rather large input space, this approach may not produce the best expected value approximations. An alternate approach is to distribute that same simulation effort over a larger sample of input points, even if it means the resulting estimates of the expected outputs at each point will be less accurate. We will show through several examples that this approach may result in better neural network metamodels; this conclusion differs from other studies involving regression metamodels.

Smartwatches support a wide range of functionality, including mediating access to sensitive data and services. However, securing access to them is difficult due to their small size—it is difficult, for example, to enter alphanumeric passwords accurately due to the limited space available to present a keyboard. Consequently, 4-digit PIN is commonly used to secure smartwatches, a technique widely acknowledged to be highly vulnerable to simple guessing attacks. To address these usability and security issues, we propose PushID, a new behavioral biometric technique for a smartwatch that combines four on-screen targets with five pressure levels to enable input of any one of 20 unique symbols from a single screen touch. In addition to this relatively large input space, PushID captures behavioral features during pressure input (e.g., finger touch profile, wrist motions) and uses this as a behavioral biometric. We report on a preliminary study of PushID and its security against random guessing attack: it achieves good usability for a single input (approximately 2 s) and high resistance to guessing (false-positive rates of 1.05%). We argue that pressure-based input can improve the security and maintain the usability of smartwatch lock systems.KeywordsSmartwatchBehavioral biometricsUsability

Cyber-Physical Systems (CPSs) have a large input space, with discrete and continuous elements across multiple layers. Hybrid Attack Graphs (HAGs) provide a flexible and efficient approach to generate attack sequences for a CPS. Analysis and testing of large-scale HAGs are prohibitively costly. To address scalability and analysis challenges of HAG generation, it is required to reduce the HAG size via sampling. Existing sampling techniques provide probabilistic sampling and do not consider the complete coverage of different types of vulnerabilities. Moreover, they do not consider the impact of successful attacks. In this work, we propose a sampling algorithm that is impact driven and coverage aware. In addition, we provide several sampling strategies for cyber-security experts to inquire about potential attacks. The experimental results performed on both synthetic and real-world graphs show that our sampling method reduces up to 50% of nodes and 86% edges while retaining all vulnerability types across different attack graph representations and graph generators.

Low efficiency as a special kind of urban construction land, which inhibits the blind expansion of urban boundaries and achieves urban construction land intensive and high efficiency. In this paper, the information feature classification of the low-cost land use was carried out by combining the Worldview-2 high resolution remote sensing images in 2011 and 2016, which was located in the central business district (CBD) of Tianhe district of Guangzhou city, China. Based on the remote sensing information extraction results of low efficiency land use of the dynamic changes. The results showed that the multi-scale segmentation was better when the segmentation scale was 15, the color parameter was 0.7, and the compactness parameter was 0.6. The low efficiency land maintained its spectral accuracy and avoided excessive fragmentation. CART classifier and SVM method had good classification results for low efficiency land, yet the results of SVM method (the total accuracy is 87.66%) were more accurate than CART classifier (the total accuracy is 80.48%), especially in green area and river. Compared with the classification results of remote sensing images in 2011 and 2016, the range of inefficient land use changed locally, and the increase of green area was more significant, which indicated that the improvement of the ecological environment in the whole area was improved greatly. In conclusion, we can effectively identify the features such as spectrum, brightness, texture and spatial geometry of low efficiency sites by using high resolution remote sensing and low efficiency monitoring of regional scale. The study can be used to provide decision support for improvement of the urban low efficiency, the internal structure optimization and the landscape ecological service.

According to the MOV reduction theory, the identity-based encryption (IBE) scheme basing on the bilinear map will lose the high efficiency of elliptic curve. So it is meaningful to build the provably secure identity-based encryption scheme, but which is without the bilinear map. In this paper, based on this existing result and our new research, an improved IBE scheme will be proposed. But it is different from previous scheme that the new scheme is without symmetric encryption, and at the same time having strong provable security. At last, we will analyze the performance of this new scheme.

By a generic transformation by Canetti, Halevi, and Katz (CHK) every Identity-based encryption (IBE) scheme implies a chosen-ciphertext secure public-key encryption (PKE) scheme. In the same work it is claimed that this transformation maps the two existing IBE schemes to two new and different chosen-ciphertext secure encryption schemes, each with individual advantages over the other.In this work we reconsider one of the two specific instantiations of the CHK transformation (when applied to the “second Boneh/Boyen IBE scheme”). We demonstrate that by applying further simplifications the resulting scheme can be proven secure under a weaker assumption than the underlying IBE scheme.Surprisingly, our simplified scheme nearly converges to a recent encryption scheme due to Boyen, Mei, and Waters which itself was obtained from the other specific instantiation of the CHK transformation (when applied to the “first Boneh/Boyen IBE scheme”). We find this particularly interesting since the two underlying IBE schemes are completely different.The bottom line of this paper is that the claim made by Canetti, Halevi, and Katz needs to be reformulated to: the CHK transformation maps the two known IBE schemes to nearly one single encryption scheme.KeywordsEncryption SchemeMessage Authentication CodeChallenge CiphertextChoose Ciphertext AttackBilinear GroupThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

The Fujisaki-Okamoto (FO) conversion is a generic conversion to enhance a public key encryption scheme with security of one-way against chosen plaintext attacks (OW-CPA) to security of indistinguishable against adaptive chosen ciphertext attacks (IND-CCA) in the random oracle model. Existing works have shown that the FO conversion also can generically upgrade the security of the identity-based encryption (IBE) schemes or certificateless public key encryption (CL-PKE) schemes. However, it is still unknown if the same holds for certificate-based encryption (CBE) schemes. In this paper, we investigate the generic security of the CBE scheme obtained by applying the FO conversion to an arbitrary underlying OW-CBE-CPA secure CBE scheme and confirm that the FO conversion can generically convert any OW-CBE-CPA secure CBE into an IND-CBE-CCA secure CBE. We also note that the straightforward application of the FO conversion only leads to a CBE scheme with a loose reduction. To solve this problem, we propose an enhanced FO conversion which just introduces minor extra computation overhead, but results in considerably more efficient security reduction.