Information Leakage Problem Research Articles

An adversarial network reinforcement method for power data time series generation based on knowledge transfer and Rayleigh differential privacy

Abstract In order to solve the problem of sensitive information leakage of power data in machine learning and artificial intelligence processing, this paper proposes a method of civic-text data reinforcement based on time series generation adversarial network, which combines knowledge transfer and Rényi differential privacy protection framework to balance privacy and availability, while improving the accuracy of the model. First, the generative adversarial network generates training data that is similar to the real data but secure through adversarial learning, ensuring that even if the data is stolen, the original information cannot be inferred. Secondly, through the integration of recurrent neural network training and autoencoder, the dynamic and static characteristics of the power load data can be effectively captured, and the high dimension of the learning space can be reduced. In addition, knowledge distillation technology is used to guide the generator of training student models through a powerful teacher network of knowledge transfer, improving the diversity and quality of the generated data. Finally, from the four aspects of privacy, data similarity and diversity, and practicability, the practical application value and potential of the framework for electricity consumption data are comprehensively evaluated.

Time‐Dependent Wrinkle Pattern Based on Photo‐Controlled Stress Relaxation for Multi‐Level Information Encryption and Information Camouflage

AbstractStimuli‐responsive surfaces have garnered intensive research attention in addressing the challenges of severe information leakage problems, but they exhibit limitations in terms of intricate preparation and compatibility. Herein, the study reports a time‐dependent wrinkled surface controlled by photo‐induced stress relaxation of azobenzene‐ containing crosslinked polymer network comprised of furan‐containing polymer (PEA‐Fu) and maleimide‐substituted azobenzene (AZO‐2MI) as skin layer. Two kinds of wrinkled surfaces induced by two crosslinked networks (thermal‐ and UV‐induced) can be prepared by Diels‐Alder reaction and UV‐induced radical coupling reaction, exhibiting distinct amplitude decrease rate resulted from different stress relaxation rate induced by photo‐isomerization of azobenzene. By UV preprogramming with photomasks, various time‐dependent wrinkle patterns are obtained and can be identified at specific times under 450 nm irradiation. The time window for information read out can be preset through different UV preprogramming time and boundary conditions to achieve multi‐level information encryption. Furthermore, the written information can be camouflaged as another “false” information by regional‐selective irradiation under 450 nm irradiation, which offers a new method for information protection. Based on distinctive time‐dependent characteristics and excellent stability, this responsive wrinkled surface offers bright prospects in multi‐level information encryption.

Context-aware code generation with synchronous bidirectional decoder

Code generation aims to map natural language descriptions to code snippets. Recent approaches using sequence-to-tree models have shown promising results. However, they generally adopt an autoregressive way to predict the next token based on previous ones and do not consider potential future tokens. To address this issue, we propose Contextor, a novel context-sensitive model employing a bidirectional decoder to generate tokens in two different orders synchronously and interactively. Specifically, we employ two decoders to generate two sequences of different traversals and share their context knowledge via the attention mechanism. As a result, our model can synthesize both previous and future information simultaneously. To alleviate the information leakage problem caused by the teacher-forcing training strategy and bidirectional decoding, we propose an adapted scheduled sampling technique to prevent the decoders from contacting the actual label. Furthermore, Contextor also features a bidirectional beam search algorithm to better interact with both decoders. Experimental results demonstrate that our approach outperforms the state-of-the-art baselines.

Adjusting Optical Polarization with Machine Learning for Enhancing Practical Security of Continuous-Variable Quantum Key Distribution

An available trick to mitigate the interference of environmental noise in quantum communications is to modulate signals with time-polarization multiplexing. Conversely, due to effects of the atmospheric turbulence in free space, the polarization of signals fluctuates randomly, resulting in feasible information leakage when direct polarization demultiplexing is carried out at the receiver, drowning out the noise-contained signals. For enhancing the practical security of the continuous-variable quantum key distribution (CVQKD), we propose a machine learning (ML) approach for optimization of the dynamic polarization control (DPC) of signals transmitted through atmospheric turbulence. An optimal DPC scheme can be adaptively adjusted with ML algorithms, which is based on the received signals at the receiver for solving the loophole problem of information leakage since it provides an accurate response to the polarization changes regarding the anamorphic signals. The performance of the CVQKD system can be increased in terms of secret key rates and maximal transmission distance as well. Numerical simulation shows the positive effect of the ML-based DPC while taking into account the secret key rate of the CVQKD system. The ML-based DPC effectively reduces the feasibility of information leakage and hence results in an increased secret key rate of the practical CVQKD system.

A short-term wind speed prediction method utilizing rolling decomposition and time-series extension to avoid information leakage

ABSTRACT The accuracy of wind speed prediction is crucial for the efficient operation and scheduling of power grids. In recent years, many wind speed prediction methods have been proposed, but the results have always been unsatisfactory, and the model accuracy in experimental testing has always been overestimated. This study focuses on the problem of information leakage caused by the decomposition of the test and general training sets in traditional wind speed prediction methods. Using the original model without decomposition as the standard and the mean average (P MAE ) and mean squared (P MSE ) errors as evaluation metrics, the overestimation degree of information leakage on the model accuracy was quantified. The results show that when the test set is decomposed together, the accuracy of the model is significantly overestimated. Specifically, the overestimation of P MAE ranges from 40% to 55%, and that of P MSE is from 65% to 85%. In addition, a singular spectrum analysis (SSA) – rolling decomposition (RD) – convolutional neural network (CNN) – bidirectional gated recurrent unit (BiGRU) – attention mechanism (AM) model based on the RD method was proposed. First, SSA was used to denoise the wind speed sequence, and then RD was performed on the original sequence to provide input vectors for the neural network model. Then, the CNN – BiGRU – AM hybrid neural network module predicted the wind speed sequence. Finally, to suppress the impact of boundary effects on the model accuracy, a time-series extension strategy based on neural networks was incorporated into the model. An example analysis indicates that the SSA – RD – CNN – BiGRU – AM model can avoid information leakage compared with other traditional models.

A parallel differential learning ensemble framework based on enhanced feature extraction and anti-information leakage mechanism for ultra-short-term wind speed forecast

Accurate ultra-short-term prediction plays a very important role in maintaining power equipment, preventing accidents, and optimizing dispatch effectiveness. Currently, the decomposition-integration method is widely used in ultra-short-term wind speed prediction. However, most of the existing models ignore the problem of information leakage that occurs during data processing and the effect of discrepancies between multiple decomposition sequences on the prediction results, which poses a great challenge to the accuracy of wind speed prediction. Therefore, this study proposes an improved hybrid wind speed prediction framework based on an improved decomposition method, an anti-information leakage mechanism and an enhanced deep learning algorithm. First, the original sequences are processed using improved singular spectrum analysis (ISSA) to achieve an effective mining of deep features. Second, Transformer is selected to construct the input-output relationship model between the original sequence and the feature components to form an anti-information leakage mechanism. Finally, an enhanced hybrid deep learning model is built using the concept of parallel processing, which can simultaneously process subsequences of different complexity and effectively reduce the prediction error of the model. Simulation experiments are conducted using four sets of data from wind farms located in Liaoning Province, China. The results of the simulations demonstrate that the model performs better in predictions than the benchmark model.

A Data Hierarchical Encryption Scheme Based on Attribute Hiding under Multiple Authorization Centers

The data hierarchical Ciphertext-Policy Attribute-Based Encryption (CP-ABE) scheme implements multiple hierarchical data encryption of a single access policy, which reduces the computation and storage overhead. However, existing data hierarchical CP-ABE schemes have some problems, such as the leakage of personal privacy information through access policies or user attributes in plaintext form, and these schemes grant enough privileges to a single authorization center. If the authorization center is untrusted or attacked, keys can be used to illegally access data, which is the key escrow problem. To solve these problems, we propose an Attribute Hiding and Multiple Authorization Centers-based Data Hierarchical Encryption Scheme (AH-MAC-DHE). Firstly, we propose an Attribute Convergence Hiding Mechanism (ACHM). This mechanism solves the problem of personal privacy information leakage by hiding access policies and user attributes. Secondly, we design Privilege-Dispersed Multiple Authorization Centers (PD-MAC). PD-MAC solves the problem of key escrow by dispersing the privileges of the single authorization center to the user authorization center and attribute authorization center. Finally, we prove that AH-MAC-DHE is secure under the decisional q-parallel Bilinear Diffie-Hellman Exponent (BDHE) assumption, which also satisfies anti-collusion and privacy security. The experimental results indicate that compared with existing schemes, AH-MAC-DHE performs well.

Research on the Marketing Strategy of Mobile Game Applications in Chinese Game Companies Taking miHoYo as an Example

The global mobile gaming market has grown rapidly in recent years, bringing in huge profits while maintaining its growth potential. The current state of the market has attracted many large game companies to enter, while many small and medium-sized game companies are also developing new games to attract customers. In this study, the author chooses miHoYo as the research object to first analyze the current market situation and trend that miHoYo is facing based on data reports and literature, and then use the 4Ps theory to analyze miHoYos marketing strategy. In the face of the increasingly competitive but highly promising mobile game market, miHoYo mainly adopts the strategy of releasing innovative products while continuously developing new market segments; its pricing, promotion, and publicity strategies focus on game products, co-branding with other brands and platform publicity. Lastly, based on the high pressure of market competition, unsatisfactory community discussion environment and information leakage problems that miHoYo is currently facing, suggestions are provided for miHoYos future development.

Privacy-Preserving Cross-Area Traffic Forecasting in ITS: A Transferable Spatial-Temporal Graph Neural Network Approach

Traffic forecasting is essential in improving and maintaining safety and orderliness in intelligent transportation systems (ITS). As a deep learning approach, graph neural networks (GNN) based spatial-temporal association mining methods are promising in traffic forecasting. However, current GNN-based methods usually require a high number of training data, and when the sample volume is small, the performance of the model drops dramatically. The existing transfer methods can solve this problem by leveraging knowledge from other data-rich areas, but the domain adaption method with access to source data still faces the non-neglectable problem of private information leakage in the source area. A solution that can solve cross-area transfer without access to source data is still missing. In this paper, to fill the gap, we propose a Transferable Federated Inductive Spatial-Temporal Graph Neural Network (T-ISTGNN) framework to transfer spatial-temporal dependency information in cross-area data to accomplish traffic state forecasting. First, we introduce a multi-source model aggregation scheme based on federated learning to retain the traffic information of the source areas. Second, we propose a transfer method between source and target areas based on hypothesis transfer learning to achieve domain adaption under source domain data protection. Third, we propose a GNN-based method called Inductive Spatial-Temporal Graph Neural Network (ISTGNN) for traffic forecasting. Experiments on real-world datasets demonstrate that T-ISTGNN is capable of cross-area traffic state forecasting under the restriction of preserving the privacy of source areas.

Certificate Transparency With Enhanced Privacy

Digital certificates play an important role in the authentication of communicating parties for transport layer security. Recently, however, frequent incidents such as the illegal issuance of fake certificates by a compromised certificate authority have raised concerns about the legacy certificate system. Certificate Transparency (CT) mitigates such issues by employing a log server to audit issued certificates publicly, making the certificate issuance and verification processes transparent. Unfortunately, the legacy CT ecosystem suffers from log server compromises and user browsing information leakage. Furthermore, the data structure for the certificate management in the legacy CT system incurs computation overhead linear to the number of registered certificates in the log. In this paper, we propose a secure CT scheme by leveraging a shared value tree (SVT), a novel log structure specifically designed to address the log server compromise and browsing information leakage problems. The verification time of SVT remains constant regardless of the number of registered certificates in the log. We analyze our scheme on the legacy CT system to demonstrate its incremental deployability, guaranteeing a smooth transition toward a more secure web ecosystem.

Efficient fault-tolerant quantum dialogue protocols using a quantum reordering circuit of EPR pairs

This study proposes two efficient fault-tolerant quantum dialogue (QD) protocols that are robust against the collective-dephasing and collective-rotation noises, respectively. In the proposed protocol, the message carriers are decoherence-free quantum states that are resistant to the corresponding collective noise, provided that all quantum photon pairs of a transmitted unit remain within the same time window. These quantum states and their combinations are used to compose the decoy photon pairs to ensure the security of the transmission. An observation on the Bell measurement has allowed an EPR pair as a message carrier to Require only one of its photons for protection. That is, the measurement of one single photon in an EPR pair will gain no information on its actual Bell state. This property has effectively reduced the number of decoy photons in quantum transmission. Since the photons used in the message carriers are particles of EPR pairs, the proposed two fault-tolerant QD protocols required only half of the decoy photons to ensure the same level of security. In the transmission, one photon of each EPR pair is separated using a reordering mechanism, and a quantum logic circuit is designed and implemented to demonstrate the concept in practice. The reduction of decoy photons has significantly improved the qubit efficiency of the proposed QD protocols compared with other relevant existing works. Furthermore, the proposed schemes also have no information leakage problem.

Research on hierarchical network security situation awareness data fusion method in big data environment

ABSTRACT With the increase in the number of network users, data information has become more abundant, and query speed and coverage have greatly improved. In this era, the problem of information leakage is relatively serious, and some malicious software has invaded the network system of user devices, increasing the threat to network security. In order to improve the analysis ability of network security situation awareness, this study designs a hierarchical network security situation awareness data fusion method under the big data environment. On the basis of data fusion technology and network security situation awareness technology, the hierarchical network security situation information is collected. Then, its features are extracted, and the network security situation awareness process is constructed based on RBF neural. By using this process, hierarchical network security situation awareness data can be obtained. The data is first sorted out and analyzed, and then the data is filtered. Finally, the matrix three decomposition data fusion algorithm is selected as the blueprint of the data fusion method. It can complete the hierarchical network security situation awareness data fusion. Experimental results show that the proposed method has high matching accuracy, little influence on the data fusion time due to the increase of data volume and low energy consumption in the fusion process.

Multimodal Fusion-Based Image Hiding Algorithm for Secure Healthcare System

The development of artificial intelligence (AI) plays a significant role of multimedia applications, especially in the healthcare domain. However, it has brought about the problem of sensitive information leakage. To address these challenges, an interesting multimodal fusion-based robust image hiding algorithm is proposed in this paper. Firstly, fused image is considered as mark image generated from MRI and CT images using non-subsampled shearlet transform (NSST). Secondly, we employed principal component analysis (PCA) to compute the appropriate coefficients of cover image for embedding purpose. Thirdly, fused mark image is Arnold cat map encoded to address the security issue hidden mark media. Finally, the fusion of fractional dual-tree complex wavelet transform (Fr-DTCWT) and randomized singular value decomposition (RSVD) is utilized to conceal encrypted fused mark media inside host image. Our findings show that the proposed algorithm outperforms some of the recent techniques in terms of high robustness and invisibility.

Video Captioning by Learning from Global Sentence and Looking Ahead

Video captioning aims to automatically generate natural language sentences describing the content of a video. Although encoder-decoder-based models have achieved promising progress, it is still very challenging to effectively model the linguistic behavior of humans in generating video captions. In this paper, we propose a novel video captioning model by learning from gLobal sEntence and looking AheaD, LEAD for short. Specifically, LEAD consists of two modules: a Vision Module (VM) and a Language Module (LM) . Thereinto, VM is a novel attention network, which can map visual features to high-level language space and model entire sentences explicitly. LM can not only effectively make use of the information of the previous sequence when generating the current word, but also have a look at the future word. Therefore, based on VM and LM, LEAD can obtain global sentence information and future word information to make video captioning more like a fill-in-the-blank task than a word-by-word sentence generation. In addition, we also propose an autonomous strategy and a multi-stage training scheme to optimize the model, which can mitigate the problem of information leakage. Extensive experiments show that LEAD outperforms some state-of-the-art methods on MSR-VTT, MSVD, and VATEX, demonstrating the effectiveness of the proposed approach in video captioning. In addition, we release the code of our proposed model to be publicly available. 1

Promotion Mechanism to Information Protection through Virtual-Real Fusion Network

To address the increasingly serious information leakage problem, the quantitative mechanism of implementing information protection was explored using information fragmentation tools and the general mechanism of enhancing information protection strength was examined in the virtual-real fusion network. Information fragmentation protection is fragmenting the information content sufficiently and decentralizing the store address sufficiently to prevent others from illegally accessing the information content. The validation analysis using arithmetic examples proved the effectiveness and feasibility of the constructed model for improving information protection. Compared with the virtual network, the virtual-real fusion network can increase the information fragmentation protection strength strength to the infinite status.

A two-stage federated optimization algorithm for privacy computing in Internet of Things

With the advent of the Internet of things (IoT) era, federated learning plays an important role in breaking through traditional data barriers and effectively realizing data privacy and security in the process of sharing. However, the demand of the practical problems makes the algorithm still have great challenges in effectively balancing various factors, such as privacy security, accuracy, computing efficiency and so on. To challenge this problem, a two-stage federated optimization algorithm based on robust and multitasking learning is designed. In optimization client local model stage, an adaptive weight assignment mechanism is adopted to guide robust learning based on multiple untrusted sources data, which aims to ensure the credibility and robustness of the client model and obtain a reliable client model. To address the information leakage problem during the server–client global model optimization stage, a privacy patch layer is added to the client local model in multitask learning and maintain its privacy parameters stored on the client model during the global model parameter aggregation process, which aims to meet the personalized requirements and performance requirements of protecting the model privacy. To effectively measure the performance of our algorithm, two extensive experiments are carried out to verify the robustness and accuracy of model under different datasets, respectively. In addition, simulation results show that our algorithm successfully suppresses the impact of corrupted or irrelevant sources on performance, and its performance is better than the other two robust distributed learning baseline methods in the client local model optimization stage. At the same time, our algorithm achieves better accuracy performance than other advanced personalized optimization algorithms in the server–client global model optimization stage. Finally, it achieves a good balance between robustness, computational efficiency and model privacy protection.

Secure channel free public key authenticated encryption with multi-keyword search on healthcare systems

A significant amount of electronic health records (EHRs) have been kept in the cloud due to the quick development of healthcare information systems. However, storing EHRs in the cloud may raise security and data privacy issues. Because of the privacy of a patient’s medical data, storing it on a cloud server requires encryption. Public-key encryption with keyword search (PEKS) solves the problem of retrieval on ciphertext, thus avoiding the problem of plaintext information leakage. However, most PEKS schemes are susceptible to inside keyword guessing attacks (KGA). Besides, transmitting the trapdoor to the cloud server requires a secure channel, which is impractical in healthcare information systems. Roughly speaking, PEKS does not protect the privacy of trapdoor and it is expensive to establish secure channels. In this paper, we propose a secure-channel free public key authenticated encryption with multi-keyword search (SCF-PAEMKS) scheme. The proposed SCF-PAEMKS scheme supports conjunctive keyword search, meanwhile satisfying multi-ciphertext indistinguishability (MCI) and multi-trapdoor privacy (MTP) simultaneously. The efficiency analysis and experimental results show that our SCF-PAEMKS scheme enjoys a better performance compared with related schemes.

Information Leakage Tracking Algorithms in Online Social Networks

Aim: In order to explore the study on information leakage tracking algorithms in online social networks, solve the problem of information leakage in the current online social network. a deterministic leaker tracking algorithm based on digital fingerprints is proposed Background: : First, the basic working principle of the algorithm is that the platform uses plug-ins to embed a unique user-identifying information before users try to obtain digital media such as images and videos shared by others on the platform. Objective: Secondly, because the scale of users in social networks is extremely large and dynamic, while ensuring the uniqueness of digital fingerprints, it is also necessary to ensure the coding efficiency and scalability of digital fingerprint code words. Methods: Simulation experiments show that: 10 experiments are performed on 10,000 to 100,000 nodes, the Hamming distance threshold d is set to be 3, and the length of the hash code and the binary random sequence code are both 64 bits. Results: Compared with the traditional linear search, the proposed digital fingerprint fast detection scheme has better performance Conclusion: It is proved that an index table based on hash code and user ID is established and combines with community structure, to improve the detection efficiency of digital fingerprints

Black holes as clouded mirrors: the Hayden-Preskill protocol with symmetry

The Hayden-Preskill protocol is a qubit-toy model of the black hole information paradox. Based on the assumption of scrambling, it was revealed that quantum information is instantly leaked out from the quantum many-body system that models a black hole. In this paper, we extend the protocol to the case where the system has symmetry and investigate how the symmetry affects the leakage of information. We especially focus on the conservation of the number of up-spins. Developing a partial decoupling approach, we first show that the symmetry induces a delay of leakage and an information remnant. We then clarify the physics behind them: the delay is characterized by thermodynamic properties of the system associated with the symmetry, and the information remnant is closely related to the symmetry-breaking of the initial state. These relations bridge the information leakage problem to macroscopic physics of quantum many-body systems and allow us to investigate the information leakage only in terms of physical properties of the system.

ZPA: A Smart Home Privacy Analysis System Based on ZigBee Encrypted Traffic

Currently, the ZigBee protocol is widely used in smart homes and provides convenience to people. However, smart home devices often carry a large amount of real physical world information, which may result in information leakage problems. In this paper, to reveal the privacy security issues existing in ZigBee-based smart home networks, we design a smart home privacy analysis system based on ZigBee-encrypted traffic, called ZPA. ZPA can extract ZigBee data features based on the device’s operating mode and time window and use state-of-the-art machine-learning models to identify the type and status of smart home devices that could leak users’ private information. Through the analysis of 20 different devices from 5 manufacturers, the results show that even if the ZigBee traffic is protected by encryption, the accuracy of the proposed method in device type identification and state inference can reach approximately 93% and 98%, respectively. The types and statuses of devices in smart homes will reveal the user’s activity information to a certain extent. The privacy security of ZigBee-based smart devices still needs to be further strengthened.