Random Noise Injection Research Articles

Privacy-Preserving Process Mining: A Blockchain-Based Privacy-Aware Reversible Shared Image Approach

ABSTRACT Deeper integration of cross-organizational business process sharing and process mining has advanced the Industrial Internet. Privacy breaches and data security risks limit its use. Scrambling or anonymizing event data frequently preserves privacy in established studies. The scrambling mechanism or random noise injection corrupts event log process information and lowers process mining outcomes. This research presents a blockchain-based privacy-aware reversible shared image approach using chaotic image and privacy-aware theory for privacy-preserving process mining. Avoiding data loss, disclosure concerns, correlation attacks, and encrypted sharing is possible with the method. First, process data is turned into color images with chaotic image encryption to safeguard privacy and allow reversible reproduction. Second, the on-chain-off-chain paradigm helps handle information lightly; finally, attribute encryption of multi-view event data for correlation resistance and on-demand data encryption sharing. Simulations on common datasets reveal that: 1. The system performance of the proposed method outperforms the baseline method by 57%. 2. The strategy greatly enhances categorical and numerical data privacy. 3. It performs better in event data privacy protection and process mining fitness and precision. The proposed method ensures the secure flow of cross-organizational information in the Industrial Internet and provides a novel privacy-secure computational approach for the growing Artificial Intelligence.

Study on noise enhancement injection in laser gyroscopes based on random noise variation rates

In the process of injecting random noise into a mechanical vibration laser gyroscope, the amplitude of the injected noise decays significantly as the noise frequency increases. To address this phenomenon, theoretical research was conducted on the transfer function of random noise in a mechanical vibration laser gyroscope, and a transfer function of random noise enhancement injection efficiency was established, which is related to the damping coefficient and resonant frequency. A method for enhancing random noise injection efficiency based on the rate of random noise level variation was proposed, and a circuit system for random noise enhancement injection in mechanical vibration laser gyroscopes was designed. The results showed that compared with the original random noise injection technology, random noise enhancement injection technology can effectively suppress the serious attenuation of high-frequency random noise amplitude, increase random noise injection efficiency by about 41.27%, reduce the random walk of the laser gyroscope's angle by about 17.73%, and improve its accuracy by about 27.02%. Random noise enhancement injection technology provides an important reference for improving the performance of mechanical vibration laser gyroscopes.

Timing and Single-pulse Study of Pulsar J1909+0122 Discovered by CRAFTS

We report the discovery of PSR J1909+0122 by the Five-hundred-meter Aperture Spherical Radio Telescope (FAST) as part of the Commensal Radio Astronomy FAST Survey. PSR J1909+0122 has a spin period of 1.257 s and a dispersion measure of 186.2 pc cm−3. The averaged pulse profile shows two distinct components. We performed a single-pulse study based on a one-hour observation at 1.25 GHz on 2021 August 23. We used a threshold of 5σ ep to measure the nulling fraction (NF) as 63% ± 1.5%. The longitude-resolved fluctuation spectra and fast Fourier transform spectra of the binary sequences revealed the quasi-periodicity of nulling with a period of 30 rotation periods. We examined the reliability of the periodicity by comparing it to random noise injection. The NF, , and modulation periodicity P M of PSR J1909+0122 were compared with other periodic nulling pulsars, showing that the source of J1909+0122 has the second largest NF in the population. Long-term timing observations over six months were used to derive the phase-connected ephemeris of this pulsar. The measured P and values disfavor dipolar geometry for polar gap models, and the prediction for a space-charge-limited flow model in the case of inverse Compton scattering is only just above the death line. In this work, PSR J1909+0122 has revealed possible correlations between nulling behavior and pulsar properties, which will help to shed light on the pulsar emission mechanism and its temporal evolution in future observations.

Efficient noise injection for exposing hidden data races

The happens-before (HB) relation is widely used to precisely detect data races, which are among the major causes of concurrency bugs. However, the HB relation is sensitive to thread interleaving; thus, hidden races that can be revealed in other thread interleavings may remain despite the repeated running of a program with an HB detector. In this paper, we propose a noise injection-based race detection technique (RaceInducer) that can expose and detect hidden races by using the runtime HB analysis result to determine the noise injection instants. RaceInducer delays a thread execution after the occurrence of the first no-lock non-racing accesses of three types (read-exclusive, write-exclusive, and read-shared) to a shared variable during a precise HB detection (FastTrack). Using this technique, RaceInducer can efficiently disturb the normal locking sequences between two threads that generate HB edges hiding data races. We implemented RaceInducer on the RoadRunner instrumentation framework for Java and evaluated its detection capability and runtime overheads on 12 real-world benchmark programs. The evaluation results showed that RaceInducer generated a lower runtime overhead compared to random noise injection that randomly injected noise for no-lock non-racing accesses and can detect more races with the same noise strength. RaceInducer generates particularly lower runtime overhead than the random noise injection for large programs that generates a high number of shared-memory accesses. In addition, we experimentally demonstrated that RaceInducer incurred lower runtime overheads than a current hidden-race detector (DrFinder) for programs with a relatively large number of locking operations.

On curvelet CS reconstructed MR images and GA-based fuzzy conditional entropy maximization for segmentation

In many practical situations, magnetic resonance imaging (MRI) needs reconstruction of images at low measurements, far below the Nyquist rate, as sensing process may be very costly and slow enough so that one can measure the coefficients only a few times. Segmentation of such subsampled reconstructed MR images for medical analysis and diagnosis becomes a challenging task due to the inherent complex characteristics of the MR images. This paper considers reconstruction of MR images at compressive sampling (or compressed sensing (CS)) paradigm followed by its segmentation in an integrated platform. Image reconstruction is done from incomplete measurement space with random noise injection iteratively. A weighted linear prediction is done for the unobserved space followed by spatial domain denoising through adaptive recursive filtering. The reconstructed images, however, suffer from imprecise and/or missing edges, boundaries, lines, curvatures etc. and residual noise. Curvelet transform (CT) is purposely used for removal of noise and for edge enhancement through hard thresholding and suppression of approximate subbands, respectively. Then a fuzzy entropy-based clustering, using genetic algorithms (GAs), is done for segmentation of sharpen MR Image. Extensive simulation results are shown to highlight performance improvement of both image reconstruction and segmentation of the reconstructed images along with relative gain over the existing works.

Preserving Data Confidentiality and QueryPrivacy Using KNN-R Approach

Cloud computing is one of the famous and well known technique that processes the data query efficiently. Since it is maintaining huge amount of resources, its privacy and security is an issue. Cloud service providers are not trust worthy, so data is to be secured. Whenever the data is sent to the cloud, it is encrypted because to protect the sensitive data such that query privacy and data confidentiality is assured. Cloud computing reduces the inhouse resources .This doesn’t mean processing of the query should be slow. To ensure query privacy and data confidentiality RASP approach is designed. The RASP Perturbation technique combines Order preserving Encryption, Dimensionality Expansion, random noise injection, random projection to provide strong safety to the perturbed data and query. RASP makes use of the KNN algorithm to process the query efficiently. KNN approach use the minimum square range to process the query. It transfers data to the multidimensional space where it uses indexing approach to process the minimum square range points.

Preserving Data Confidentiality and Query Privacy Using KNNR Approach

Cloud computing is one of the famous and well known technique that processes the data query efficiently. Since it is maintaining huge amount of resources, its privacy and security is an issue. Cloud service providers are not trust worthy, so data is to be secured. Whenever the data is sent to the cloud, it is encrypted because to protect the sensitive data such that query privacy and data confidentiality is assured. Cloud computing reduces the in- house resources .This doesn't mean processing of the query should be slow. To ensure query privacy and data confidentiality RASP approach is designed. The RASP Perturbation technique combines Order preserving Encryption, Dimensionality Expansion, random noise injection, random projection to provide strong safety to the perturbed data and query. RASP makes use of the KNN algorithm to process the query efficiently. KNN approach use the minimum square range to process the query. It transfers data to the multidimensional space where it uses indexing approach to process the minimum square range points.

Building Confidential and Efficient Query Services in the Cloud with RASP Data Perturbation

With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. On the other hand, a secured query service should still provide efficient query processing and significantly reduce the in-house workload to fully realize the benefits of cloud computing. We propose the random space perturbation (RASP) data perturbation method to provide secure and efficient range query and kNN query services for protected data in the cloud. The RASP data perturbation method combines order preserving encryption, dimensionality expansion, random noise injection, and random projection, to provide strong resilience to attacks on the perturbed data and queries. It also preserves multidimensional ranges, which allows existing indexing techniques to be applied to speedup range query processing. The kNN-R algorithm is designed to work with the RASP range query algorithm to process the kNN queries. We have carefully analyzed the attacks on data and queries under a precisely defined threat model and realistic security assumptions. Extensive experiments have been conducted to show the advantages of this approach on efficiency and security.

Asynchronous Advanced Encryption Standard Hardware with Random Noise Injection for Improved Side-Channel Attack Resistance

This work presents the design, hardware implementation, and performance analysis of novel asynchronous AES (advanced encryption standard) Key Expander and Round Function, which offer increased side-channel attack (SCA) resistance. These designs are based on a delay-insensitive (DI) logic paradigm known as null convention logic (NCL), which supports useful properties for resisting SCAs including dual-rail encoding, clock-free operation, and monotonic transitions. Potential benefits include reduced and more uniform switching activities and reduced signal-to-noise (SNR) ratio. A novel method to further augment NCL AES hardware with random voltage scaling technique is also presented for additional security. Thereby, the proposed components leak significantly less side-channel information than conventional clocked approaches. To quantitatively verify such improvements, functional verification and WASSO (weighted average simultaneous switching output) analysis have been carried out on both conventional synchronous approach and the proposed NCL based approach using Mentor Graphics ModelSim and Xilinx simulation tools. Hardware implementation has been carried out on both designs exploiting a specified side-channel attack standard evaluation FPGA board, called SASEBO-GII, and the corresponding power waveforms for both designs have been collected. Along with the results of software simulations, we have analyzed the collected waveforms to validate the claims related to benefits of the proposed cryptohardware design approach.

The Metabolic Status Drives Acclimation of Iron Deficiency Responses in Chlamydomonas reinhardtii as Revealed by Proteomics Based Hierarchical Clustering and Reverse Genetics

Iron is a crucial cofactor in numerous redox-active proteins operating in bioenergetic pathways including respiration and photosynthesis. Cellular iron management is essential to sustain sufficient energy production and minimize oxidative stress. To produce energy for cell growth, the green alga Chlamydomonas reinhardtii possesses the metabolic flexibility to use light and/or carbon sources such as acetate. To investigate the interplay between the iron-deficiency response and growth requirements under distinct trophic conditions, we took a quantitative proteomics approach coupled to innovative hierarchical clustering using different "distance-linkage combinations" and random noise injection. Protein co-expression analyses of the combined data sets revealed insights into cellular responses governing acclimation to iron deprivation and regulation associated with photosynthesis dependent growth. Photoautotrophic growth requirements as well as the iron deficiency induced specific metabolic enzymes and stress related proteins, and yet differences in the set of induced enzymes, proteases, and redox-related polypeptides were evident, implying the establishment of distinct response networks under the different conditions. Moreover, our data clearly support the notion that the iron deficiency response includes a hierarchy for iron allocation within organelles in C. reinhardtii. Importantly, deletion of a bifunctional alcohol and acetaldehyde dehydrogenase (ADH1), which is induced under low iron based on the proteomic data, attenuates the remodeling of the photosynthetic machinery in response to iron deficiency, and at the same time stimulates expression of stress-related proteins such as NDA2, LHCSR3, and PGRL1. This finding provides evidence that the coordinated regulation of bioenergetics pathways and iron deficiency response is sensitive to the cellular and chloroplast metabolic and/or redox status, consistent with systems approach data.