Differential privacy protection scheme based on community density aggregation and matrix perturbation

Conventional second harmonic current restriction approach is an adopted method which has been implemented to transformer differential protection schemes. Because of unreliable nature of this approach in discrimination between inrush current, internal faults and external faults and due to the saturation of current transformers, we present new features of the differential protection scheme. This helps to determine the feasibility and applicability of the proposed approach in differential protective scheme for power transformer. The Current Transformer Saturation (CTS) phenomenon issue and its revelation is one of the most significant problems in power system protection particularly in the differential protection. In this paper, the saturation phenomenon and its effects on the differential protection are analyzed. Prevalent algorithms in digital signal processing which are implemented in power system cannot correctly recognize this phenomenon. Mal-operation of differential protection relay takes place when saturation in CT is appeared. This paper presents a Third-Difference Algorithm (TDA) for CTS recognition. In order to make comparison between the presented approach and prevalent digital protective algorithm, an identical case study is simulated. Simulation results also show that the TDA is both satisfactory and consistent with expectation. Keywords: Current transformer, Saturation phenomenon, Third-difference Algorithm, Internal and external faults.

Microgrids are small networks of loads and sources that can operate either connected to the grid or islanded. In order to operate in islanded mode, the microgrid must have a new protection scheme as classical distribution protection is inadequate. This work develops a differential zone protection scheme that works by comparing current measurements from several devices on the boarders of the zones. This method is as effective at detecting microgrid faults as digital relay differential protection schemes at significantly lower costs. The optimal placement of zones and devices is formulated. A case study shows that the optimal placement solution can reduce costs by more than 50% when compared to having a relay at each line segment.

Social networks contain sensitive information, and direct press conferences disclose people’s privacy. The published social network data needs privacy protection. The currently released social network data has seriously damaged the network structure due to privacy protection. This paper proposes a social network graph publishing model that preserves the social network structure. This model builds an uncertainty graph based on privacy protection probability to generate social network data for publication. It is proved theoretically that the social network graph publishing model satisfies the definition of differential privacy. This paper uses three real social network datasets to evaluate the effectiveness of the proposed method, showing that the method effectively retains the network structure.

Differential privacy is often used in location privacy protection because of its strict reasoning and proof privacy guarantee. When users make continuous location query, it will cause noise superposition, which leads to the decline of query accuracy. At present, although differential privacy based on rule tree structure can reduce the query error, it will generate a lot of invalid zero nodes. The data structure is too large, and more improvement in the query accuracy can be further investigated. In this paper, we proposed a differential privacy location privacy protection method based on generative adversary network. Firstly, the definition of location data privacy protection under differential privacy mechanism is given, and then resume density aware network under differential privacy mechanism. Based on density aware network, the privacy protection problem of location data can be transformed into the distribution of fitting trajectory length. Finally, we use Markov chain to generate a new trajectory, and introduce the generative adversarial network to construct a set differential privacy protection method. Compared with other methods to improve the accuracy of differential privacy query, this method can effectively reduce the problem of query accuracy decline caused by noise superposition in continuous query, and can adapt to lbs location query service in different density environments.KeywordsGenerative adversarial networkLocation differencePrivacy protectionMarkov chainDensity aware network

In response to the privacy leakage risks inherent in the big data processing of power user personas, propose a collaborative optimization‐based joint privacy protection mechanism for Differential Privacy (DP) and Secret Sharing (SS). This method reveals the inherent correlation between DP noise parameters and SS sharding strategy and designs an adaptive noise sharding mapping mechanism to achieve joint optimization of privacy protection and communication overhead. Based on this mechanism, a specific privacy protection scheme for power user personas is designed. Experimental results demonstrate that the DP + SS privacy protection method designed in this study achieves an accuracy rate of 93.14% on the MNIST dataset and maintains an accuracy rate of 92.06% across multiple datasets, indicating that the scheme effectively quantifies the level of privacy protection. The privacy protection level of the scheme is quantitatively defined through differential privacy budget ε ∈ [0.11,0.15], secret sharing ( k = 3, n = 5) threshold mechanism, and privacy leakage probability δ = 10 −5 . The privacy budget is dynamically allocated through the correlation between differential privacy noise variance and the minimum reconstruction threshold of secret sharing, ensuring that privacy risks are controllable and quantifiable. At the same time, the average training time for servers is about 186.4 s, which refers to the total end‐to‐end training time in a four‐server cluster environment. Compared with a single server encryption scheme, the efficiency is improved by 48.6%. The communication overhead has increased to 10.6GB, which is the total chip transmission overhead per 10,000 users. However, the calculation stability can be maintained through load balancing, indicating that the scheme has certain feasibility. The aforementioned research results not only provide a viable applied cryptographic technology solution for privacy protection in power big data but also offer new insights into balancing privacy protection strength and data availability. More importantly, the joint privacy protection mechanism proposed by the research institute can serve as a fundamental enabling technology for the infrastructure of a secure smart grid, providing quantifiable and trustworthy privacy and security guarantees for advanced data‐driven applications such as precise load prediction, demand‐side response, and collaborative optimization of distributed energy, thereby supporting the reliable and intelligent development of the smart grid.

The differential protection of transformers sometimes does not have reliable performance facing some faults and events such as saturation of current transformers (CTs) during internal and external faults, inrush currents, and cross‐country faults (CCFs). In this paper, a new transformer differential protection scheme based on the self‐adaptive biased characteristic curve and considering CCFs and various disturbances are used. First, the maximum overlap discrete wavelet transform (MODWT) was used to extract the superior characteristics of sampled differential current of the transformer at different levels. Then, by calculating the duty cycle (D) values of energy coefficients extracted in each periodicity by MATLAB software, a new self‐adaptive biased characteristic curve will be obtained in different operating conditions. Finally, the percentage differential protection operation is adjusted based on the new adaptive characteristic curve zones. Differential current sampling under different operating conditions was performed on a real three‐phase transformer in EMTP software. The simulation results show that the proposed adaptive differential protection scheme can accurately detect various faults and events occurring in the transformer and increase system reliability.

With the development of Internet of things, more and more personal information needs to be collected in the process of applying Internet of things technology, which brings about the problem of data privacy protection. The algorithm proposed in this paper uses the differential privacy model to process the information network collected by the wireless sensor network terminal in the perception layer of the Internet of things and transmits it to the network layer to achieve the purpose of information protection. In order to reduce the added differential privacy noise, this paper firstly makes dynamic community discovery of the network graph, and adds more noise to the edge of nodes within the community. Then the hierarchical random graph model is used to represent the network graph, and the noise is added to the connection probability between the nodes in the graph, instead of adding noise to the edge directly. Considering the dynamic change of the information collected by the wireless sensor network, this paper proposes a time window partition and dynamic network community discovery algorithm, and publishes a purification map for each time window. Through hierarchical sampling, the time cost is reduced and the cumulative error is reduced, while the network structure characteristics of the time window are maintained as far as possible. Theoretical analysis and experiments show that the algorithm proposed in this paper can preserve the important network structure characteristics of the original graph under the premise of satisfying the differential privacy protection model.

In this paper, a novel differential protection scheme based on DC transients of the fault currents is proposed for the protection of the transmission system. The polarity of the fault current DC transients are used for identifying different types of fault. Certain issues like operation under high resistance faults and line charging current in which conventional differential protection maloperates are discussed. The proposed differential technique using DC transients possesses higher sensitivity and security towards high resistance faults and line charging currents. A 10 bus system is considered and simulated using PSCAD® for illustating the efficacy of the proposed differential protection.

The differential protection scheme is the most effective way to solve the protection problem of distribution network with DGs. Limited to the level of distribution network communication automation, traditional differential protection scheme can't be applied in distribution network with DGs. To solve the problem above, this paper proposes a fault synchronous identification technology. As the moment of fault is the same time for both ends of line, the technology uses the moment of fault as a time reference. The measurement about the interval between the first zero point of fundamental frequency current wave rising edge (or falling edge) after fault and the moment of fault will be carried out, and then intervals will be used to calculate phase change, which can effectively overcome the difficulty of high precision synchronous sampling. Based on fault synchronous identification technology, this paper proposes sufficient differential protection compound criterion. Only amplitude of fundamental current wave and time information after the failure are used to structure braking equation. Besides the sufficient current amplitude comparison criterion is lead up. New criterion overcomes contradiction between improving phase shift restraint capacity and sensitivity at the same time. Simulation verified the accuracy of the synchronous identification technology, the new criterion based on which has good action features too.

This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and differential current components are passed through a change detection filter, which senses the instant of fault inception and registers a change detection point (CDP). Subsequently, if CDP is registered for one or more phases, then half cycle data samples of the average and differential currents on either side of the CDP are passed through the proposed SST technique, which generates their respective spectral energies and a simple comparison between them detects the occurrence and type of the fault. The SST technique is also used to provide voltage and current phasors and the frequency during faults which is further utilized to estimate the fault location. The proposed technique as compared to conventional differential current protection scheme is quicker in fault detection and classification, which is least effected from bias setting, has a faster relay trip response (less than one cycle from fault incipient) and a better accuracy in fault location. The significance and accuracy of the proposed scheme have been verified extensively for faults in a standard microgrid system, subjected to a large number of operating conditions and the outputs vindicate it to be a potential candidate for real time applications.

With the rapid development of the Internet, privacy protection in big data has become an important research hotspot in the field of information security. Among them, differential privacy protection is the most widely used big data privacy protection technology today. However, the existing differential privacy protection technology cannot fully and effectively deal with the problem of high-dimensional privacy big data. Although the private data publishing method based on a Bayesian network can effectively deal with the conversion problem of high-dimensional data sets to low-dimensional data sets, this method also has some defects and deficiencies. Therefore, this paper proposes a large data source differential privacy protection algorithm (NIE-PrivBayes algorithm) based on normalized information entropy Bayesian network and proposes three improvements to the original PrivBayes algorithm. First, the original data set is protected by differential privacy on the client side. Secondly, the Bayesian network is constructed by the joint probability distribution obtained by the EM algorithm. Finally, the existing Bayesian network construction algorithm is optimized in the aspects of normalized information entropy. Based on significantly reducing the computational overhead, the effectiveness and availability of publishing high-dimensional data sets are realized under the premise of differential privacy protection.

Mobile edge computing (MEC) can effectively support various high reliability and low delay maritime applications, but security risks in computing tasks offloading exist. We describe a maritime network (MN) model based on MEC, and analyze the risk of data privacy leakage of maritime mobile terminal (MMT) during the offloading tasks. For abnormal maritime monitoring data (MMD), a data cleaning algorithm of sample density-based local outlier factor (SD-LOF) is proposed. It can effectively save the cost of data privacy protection (DPP) and improve the quality of MEC service. For data privacy leakage of MMT, an attribute sensitivity-based differential privacy (AS-DP) algorithm is proposed to balance security and availability of data and improve the differential privacy protection utility. Experimental results have verified the validity of the proposed two algorithms.

This paper presents a differential energy-based protection scheme for microgrid using Ensemble Empirical Mode Decomposition (EEMD). Current signal from both ends of the line is retrieved and processed through EEMD and first Intrinsic Mode Function (IMF) is selected for energy calculation. Differential energy is calculated for each line and a threshold is set for fault detection. IEEE 34 bus distribution system is modified as a microgrid for fault analysis and simulated on PSCAD/EMTDC platform. LG and LLLG faults are simulated at different fault location for island and grid connected mode. The results obtained shows that the proposed method can detect the fault successfully in the system.

The integration of large-scale offshore wind power, facilitated by Flexible Fractional Frequency Transmission Systems (FFFTS), presents significant challenges for traditional transmission line protection. The fault current fed by the Modular Multilevel Matrix Converter (M3C) exhibits weak-infeed and controlled characteristics during faults, severely degrading the sensitivity of conventional current differential protection. Moreover, the stringent synchronization requirement for data from both line ends further compromises reliability. To address this issue, this paper proposes a novel differential protection scheme based on the Dynamic Time Warping (DTW) algorithm. The method leverages the DTW algorithm to quantify and compare the variation trends of current waveforms on both sides of the line before and after a fault. By utilizing the pre-fault current as a reference sequence, the scheme constructs a protection criterion that is inherently insensitive to synchronization errors. A key innovation is its capability for fault identification and phase selection under weak synchronization conditions. Simulation results demonstrate that the proposed scheme operates correctly within 0.5 ms, exhibits high sensitivity with a DTW ratio significantly greater than 2.0 during internal faults, and remains stable during external faults. It also shows strong robustness against high transition resistance, noise interference, and current transformer sampling errors.

Community-based social recommendation under local differential privacy protection