Reviewerly/exHarmony huggingface dataset:
A Stochastic Gradient Descent Algorithm Based on Adaptive Differential Privacy

The application of differential privacy (DP) in federated learning can effectively protect users’ privacy from inference attacks. However, privacy budget allocation strategies in most DP schemes not only fail to be applied in complex scenarios but also severely damage the model usability. This paper designs a stochastic gradient descent algorithm based on adaptive DP, which allocates a suitable privacy budget for each iteration according to the tendency of the noise gradients. As the model parameters keep optimizing, the scheme adaptively controls the noise scale to match the decreased gradients, resizing the allocated privacy budget when too small. Compared with other DP schemes, our scheme flexibly reduces the negative effect of added noise on model convergence and consequently improves the training efficiency of the model. We implemented the scheme on five datasets (Adult, BANK, etc.) with three models (SVM, CNN, etc.) and compared it with other popular schemes in the classification accuracy and training time. Our scheme proved to be efficient and practical, which achieved 2% better than the second one in model accuracy, costing merely 4% of its training time with the 0.05 privacy budget.
Autonomous Human Navigation Using Wearable Multiple Laser Projection Suit

Ahstract- Light projection-based visual feedback navigation systems are advantageous in easily conveying direction. It also shifts the mental attention of the user from the screen to the environment, which increases safety. However, building an autonomous navigation system for fast navigation using light projection has the challenge of human motion sensitivity and tuning of navigation parameters based on human needs. In this paper, we present an autonomous wearable multiple laser projection stimulus (mLPS) based navigation system. The mLPS consists of three discrete laser lights for representing directional cues. This enables very quick switching for high-speed movements and precise navigation by visual cues. We designed a chest-based wearable suit for minimizing the sensitivity of light projection to human motion. Furthermore, we present a waypoint-based human navigation system and tuning of its navigation parameters based on both navigation performance and perceived human stress. Finally, a successful demonstration of the concept is presented.
The Role of Private Blockchain Architectures and Smart Contracts for Decentralized 5G Militarized Networks

The use of private blockchains is proposed to enhance decentralized 5G-based DoD wireless network security, auditability, governance, and overall performance. This paper explores and validates methods for leveraging smart contracts and decentralized consensus mechanisms inherent to blockchain approaches to securely enhance wireless network performance. A real-time, decentralized architectural software implementation is postulated with detailed interfaces to determine how blockchain and smart contracts apply to real-world wireless networks based on a patented approach developed by the authors. Methods are presented for decentralized wireless networks to perform network arbitration, spectrum service level agreements, and business logic through low latency blockchain transactions. Simulation results characterize the performance of typical blockchain transaction latencies in decentralized wireless network paradigms to determine feasibility. Use case studies of federal and non-federal spectrum sharing are discussed that demonstrate how a private decentralized blockchain architecture and smart contracts can provide considerably improved wireless network performance.
CIPhy

Occupant information inference with IoT sensor data enables many smart applications, such as patients'/older adults' in-home monitoring. The difficulty of collecting labeled real-world IoT sensor data often leads to reliability and scalability issues for those systems. Extensive prior works (e.g., domain adaptation) focus on the domain shift issues, i.e., the inconsistent data feature and label relationship, and dataset bias is often neglected. Dataset bias is commonly caused by limited and varied accessibility to labeled data for each class, and it is inevitable for real-world datasets. The model trained with a biased dataset fits into the bias, hence cannot further generalize to the testing data for accurate inference.
Learn to efficiently exploit cost maps by combining RRT* with Reinforcement Learning

Safe autonomous navigation of robots in complex and cluttered environments is a crucial task and is still an open challenge even in 2D environments. Being able to efficiently minimize multiple constraints such as safety or battery drain requires the ability to understand and leverage information from different cost maps. Rapid-exploring random trees (RRT) methods are often used in current path planning methods, thanks to their efficiency in finding a quick path to the goal. However, these approaches suffer from a slow convergence towards an optimal solution, especially when the planner's goal must consider other aspects like safety or battery consumption besides simply achieving the goal. Therefore, it is proposed a sample-efficient and cost-aware sampling RRT* method that can overcome previous methods by exploiting the information gathered from map analysis. In particular, the use of a Reinforcement Learning agent is leveraged to guide the RRT* sampling toward an almost optimal solution. The performance of the proposed method is demonstrated against different RRT* implementations in multiple synthetic environments.
Thermal-to-Color Image Translation for Enhancing Visual Odometry of Thermal Vision

A panoptic perception-based generative adversarial network for thermal-to-color image translation is proposed to demonstrate its potential as an image sequence enhancement for monocular visual odometry in blurry and low-resolution thermal vision. The pre-trained panoptic segmentation model is utilized to obtain the panoptic perception (i.e., bounding boxes, categories, and masks) of the image scene to guide the alignment between the object content codes of the original thermal domain and panoptic-level style codes sampled from the target color style space. A feature masking module further refines the style-aligned object representations for sharpening object boundaries to synthesize higher fidelity translated color image sequences. The extensive experimental evaluation shows that our method outperforms other thermal-to-color image translation methods in the image quality of translated color images. We demonstrate that the enhanced image sequences significantly improve the performance of monocular visual odometry compared with dif-ferent competing methods including thermal image sequences.
Sensing-Throughput Tradeoffs with Generative Adversarial Networks for NextG Spectrum Sharing

Spectrum coexistence is essential for next generation (NextG) systems to share the spectrum with incumbent (primary) users and meet the growing demand for bandwidth. One example is the 3.5 GHz Citizens Broadband Radio Service (CBRS) band, where the 5G and beyond communication systems need to sense the spectrum and then access the channel in an opportunistic manner when the incumbent user (e.g., radar) is not transmitting. To that end, a high-fidelity classifier based on a deep neural network is needed for low misdetection (to protect incumbent users) and low false alarm (to achieve high throughput for NextG). In a dynamic wireless environment, the classifier can only be used for a limited period of time, i.e., coherence time. A portion of this period is used for learning to collect sensing results and train a classifier, and the rest is used for transmissions. In spectrum sharing systems, there is a well-known tradeoff between the sensing time and the transmission time. While increasing the sensing time can increase the spectrum sensing accuracy, there is less time left for data transmissions. In this paper, we present a generative adversarial network (GAN) approach to generate synthetic sensing results to augment the training data for the deep learning classifier so that the sensing time can be reduced (and thus the transmission time can be increased) while keeping high accuracy of the classifier. We consider both additive white Gaussian noise (AWGN) and Rayleigh channels, and show that this GAN-based approach can significantly improve both the protection of the high-priority user and the throughput of the NextG user (more in Rayleigh channels than AWGN channels).
C Program Partitioning with Fine-Grained Security Constraints and Post-Partition Verification

We address the problem of program partitioning: dividing a program into isolated compartments that communicate via remote procedure calls to follow a security policy. Existing solutions for C programs often use a simple model that offers only “sensitive or not” control and do not provide formal guarantees of partition correctness. We present a C program partitioner for security-conscious applications that addresses these shortcomings through annotation with fine-grained security constraints (chiefly, declassification of sensitive data to select parties); from these annotations, we automatically determine a partition and auto-generate code for marshaling, serialization, and remote procedure calls. We provide post-partition verification, which leverages translation validation to show that output program partitions are behaviorally equivalent to their input programs and satisfy the security policy specified by annotations. We present results that show our approach is practical when partitioning large realistic C applications with non-trivial security constraints.
Improving Text Security Classification Towards an Automated Information Guard

Information residing at different levels of security classification cannot be effectively shared across resources that may benefit from the knowledge. We discuss the challenges and potential design choices for building an automated information guard to handle such information across systems or resources with varying authorities. We carry out experiments exemplifying one possible approach, bottom-up modeling, in the text domain, with promising results.
Threat framework for 5G cellular communications

5G networks are rapidly gaining traction as the technology of choice for not only traditional cellular networks, but also new verticals such as business, industrial and military deployments. The complexity of 5G as a system of systems poses new security challenges. Even though 5G is generally viewed as more secure than previous generations such as 4G (LTE), 5G incorporates Internet technologies (e.g., service-based architectures, cloud services, virtualization), which increase the attack surface. In addition, the much larger number of devices connecting, and the new types of such devices (e.g., affecting human safety) also raise new security concerns. The urgency of protecting these networks is increased as 5G is incorporated into human safety critical infrastructure and military establishments. This paper introduces FiGHT-Five G Hierarchy of Threats, a framework of adversarial tactics and techniques applicable to the 5G system, similar to that of MITRE ATT&CK.
PointProofs, Revisited

Vector commitments allow a user to commit to a vector of length n using a constant-size commitment while being able to locally open the commitment to individual vector coordinates. Importantly, the size of position-wise openings should be independent of the dimension n. Gorbunov, Reyzin, Wee, and Zhang recently proposed PointProofs (CCS 2020), a vector commitment scheme that supports non-interactive aggregation of proofs across multiple commitments, allowing to drastically reduce the cost of block propagation in blockchain smart contracts. Gorbunov et al. provide a security analysis combining the algebraic group model and the random oracle model, under the weak n-bilinear Diffie-Hellman Exponent assumption (n-wBDHE) assumption. In this work, we propose a novel analysis that does not rely on the algebraic group model. We prove the security in the random oracle model under the n-Diffie-Hellman Exponent (n-DHE) assumption, which is implied by the n-wBDHE assumption considered by Gorbunov et al. We further note that we do not modify their scheme (and thus preserve its efficiency) nor introduce any additional assumption. Instead, we prove the security of the scheme as it is via a strictly improved analysis.
Continuously Non-malleable Codes Against Bounded-Depth Tampering

Non-malleable codes (Dziembowski, Pietrzak and Wichs, ICS 2010 & JACM 2018) allow protecting arbitrary cryptographic primitives against related-key attacks (RKAs). Even when using codes that are guaranteed to be non-malleable against a single tampering attempt, one obtains RKA security against poly-many tampering attacks at the price of assuming perfect memory erasures. In contrast, continuously non-malleable codes (Faust, Mukherjee, Nielsen and Venturi, TCC 2014) do not suffer from this limitation, as the non-malleability guarantee holds against poly-many tampering attempts. Unfortunately, there are only a handful of constructions of continuously non-malleable codes, while standard non-malleable codes are known for a large variety of tampering families including, e.g., NC0 and decision-tree tampering, AC0, and recently even bounded polynomial-depth tampering. We change this state of affairs by providing the first constructions of continuously non-malleable codes in the following natural settings:
Compact FE for Unbounded Attribute-Weighted Sums for Logspace from SXDH

This paper presents the first functional encryption $$(\textsf{FE})$$ scheme for the attribute-weighted sum $$(\textsf{AWS})$$ functionality that supports the uniform model of computation. In such an $$\textsf{FE}$$ scheme, encryption takes as input a pair of attributes (x, z) where the attribute x is public while the attribute z is private. A secret key corresponds to some weight function f, and decryption recovers the weighted sum f(x)z. This is an important functionality with a wide range of potential real life applications, many of which require the attribute lengths to be flexible rather than being fixed at system setup. In the proposed scheme, the public attributes are considered as binary strings while the private attributes are considered as vectors over some finite field, both having arbitrary polynomial lengths that are not fixed at system setup. The weight functions are modelled as Logspace Turing machines. Prior schemes [Abdalla, Gong, and Wee, CRYPTO 2020 and Datta and Pal, ASIACRYPT 2021] could only support non-uniform Logspace. The proposed scheme is built in asymmetric prime-order bilinear groups and is proven adaptively simulation secure under the well-studied symmetric external Diffie-Hellman ( $$\textsf{SXDH}$$ ) assumption against an arbitrary polynomial number of secret key queries both before and after the challenge ciphertext. This is the best possible level of security for $$\textsf{FE}$$ as noted in the literature. As a special case of the proposed FE scheme, we also obtain the first adaptively simulation secure inner-product FE ( $$\textsf{IPFE}$$ ) for vectors of arbitrary length that is not fixed at system setup. On the technical side, our contributions lie in extending the techniques of Lin and Luo [EUROCRYPT 2020] devised for payload hiding attribute-based encryption (ABE) for uniform Logspace access policies avoiding the so-called “one-use” restriction in the indistinguishability-based security model as well as the “three-slot reduction” technique for simulation-secure attribute-hiding FE for non-uniform Logspace devised by Datta and Pal [ASIACRYPT 2021] to the context of simulation-secure attribute-hiding FE for uniform Logspace.
A Universally Composable Non-interactive Aggregate Cash System

Mimblewimble is a privacy-preserving cryptocurrency, providing the functionality of transaction aggregation. Once certain coins have been spent in Mimblewimble, they can be deleted from the UTXO set. This is desirable: now storage can be saved and computation cost can be reduced. Fuchsbauer et al. (EUROCRYPT 2019) abstracted Mimblewimble as an Aggregate Cash System (ACS) and provided security analysis via game-based definitions. In this paper, we revisit the ACS, and focus on Non-interactive ACS, denoted as NiACS. We for the first time propose a simulation-based security definition and formalize an ideal functionality for NiACS. Then, we construct a NiACS protocol in a hybrid model which can securely realize the ideal NiACS functionality in the Universal Composition (UC) framework. In addition, we propose a building block, which is a variant of the ElGamal encryption scheme that may be of independent interest. Finally, we show how to instantiate our protocol, and obtain the first NiACS system with UC security.
A Visual Tool for Interactively Privacy Analysis and Preservation on Order-Dynamic Tabular Data

The practice of releasing individual data, usually in tabular form, is obligated to prevent privacy leakage. With rendered privacy risks, visualization techniques have greatly prompted the user-friendly data sanitization process. Yet, we point out, for the first time, the attribute order (i.e., schema) of tabular data inherently determines the risk situation and the output utility, while is ignored in previous efforts. To mitigate this gap, this work proposes the design and pipeline of a visual tool (TPA, Tabular Privacy Assistant) for nuanced privacy analysis and preservation on order-dynamic tabular data. By adapting data cube structure as the flexible backbone, TPA manages to support real-time risk analysis in response to attribute order adjustment. Novel visual designs, i.e., data abstract, risk tree, integrated privacy enhancement, are developed to explore data correlations and acquire privacy awareness. We demonstrate TPA’s effectiveness with a case study on the prototype and qualitatively discuss the pros and cons with domain experts for future improvement.
Knowledge Graph Enhanced Web API Recommendation via Neighbor Information Propagation for Multi-service Application Development

In cloud era, Web APIs have been the best carrier for service delivery, capability replication and data output in multi-service application development. Currently, the number of Web APIs on the Internet is huge and growing exponentially. To enable accurate and fast Web API selection for developers, researchers have proposed a variety of Web API recommendation methods. However, existing methods cannot solve the inherent data sparsity problem well. In addition, existing methods use context information indirectly by finding neighbors or discretely through embedding techniques, while rich semantic information in the Web API ecosystem is ignored. To solve the above problems, we firstly crawl and analyze Web API data to construct a Web API knowledge graph, which laid a data foundation for alleviating the data sparsity problem. Then, we propose a knowledge graph-enhanced Web API recommendation model, so as to improve recommendation accuracy by capturing high-order structural information and semantic information. Typically, multivariate representations of user and Web API are made by the neighbor information propagation in Web API knowledge graph. The proposed model supports end-to-end learning for beneficial feature extraction. Finally, experiments results demonstrate the proposed model outperforms baselines significantly, thereby promoting the development of Web API economy.
One-Time Anonymous Certificateless Signcryption Scheme Based on Blockchain

The rapid increase of users in the blockchain makes it possible to exchange a large amount of data every moment. Since the information published on blockchain is recorded and cannot be tampered with, there is the problem of leaking the real identity of users under the big data clustering attack. Meanwhile, the existing key generation center (KGC) needs a secure channel to transmit partial private keys, which makes partial private keys depending on the channel of interaction and creates a private key security problem. In this paper, we propose a one-time anonymous certificateless signcryption (OTACLSC) scheme based on blockchain. We securely use the public channel to improve the security of the private key. By constructing a one-time pseudonym public key in the blockchain to achieve anti-identity leakage, the communication initiator constructs the pseudonym public key of both communication parties to avoid the reuse of the pseudonym public key. Then we prove the security of the scheme under the random oracle model and compare it with other schemes. Our scheme has less computation cost and shorter ciphertext length while maintaining more reliable security.
Research on the Update Method of CP-ABE Access Control Strategy Based on Smart Contract

The CP-ABE access control method based on blockchain realizes fine-grained access control of data and ensures the storage of ciphertext security. However, there are some problems in updating the access control policy. For example, the blockchain cannot be used as ample data storage. The ciphertext size increases with the number of attributes and the complexity of the access control policy, and the CP-ABE based on bilinear mapping is expensive to calculate. Therefore, this paper proposes dividing the ciphertext into data-related ciphertext and policy-related ciphertext, which are stored in blockchain and IPFS. It is worth noting that this paper uses the RSA-based CP-ABE encryption method, which can effectively reduce the computational cost of encryption and decryption and achieve a constant ciphertext size. In addition, we also systematically analyze and compare the advantages and costs generated by CP-ABE based on bilinear mapping and CP-ABE based on RSA. Through experimental analysis, security analysis, and formal analysis, compared with the existing access control policy update methods, the scheme proposed in this paper shows better performance when frequently updating the access control policy.
Technogenic geochemical abnormalities in the soils and bottom sediments of Voronezh

Environmental pollution in large industrialized cities has become an urgent global problem. On the territory of the Russian Federation, a tense ecological situation is developing in almost all cities with a population of over one million and in large administrative centers. The creation of a quality urban environment is a national priority, a complex and financially costly task, the solution of which cannot be postponed any longer. The main sources of problems in the territories occupied by cities lie in the concentration of production capacities, the steady growth of the population, and as a result, the amount of transport. Due to production, industrial cities concentrate substances of natural and man-made origin forming local and even regional geochemical anomalies in various landscapes. Among all the interrelated components, a special place belongs to the depositing (accumulating) media–soils and bottom sediments. Based on the results of geochemical studies of 2018–2020, the indicators of the quality of the soils of Voronezh were determined, the levels of the content of pollutants of I-III hazard classes in the soils of urban and peripheral areas of Voronezh (benzo(a)pyrene and heavy metals) were established. The authors provide an assessment of the accumulation of pollutants in the bottom sediments of the Voronezh water reservoir, which is located on the territory of the urban district.