Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

Nowadays, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several hybrid perturbation strategies that may be used to protect data privacy. For this, two perturbation-based techniques named improved random projection perturbation (IRPP) and enhanced principal component analysis-based technique (EPCAT) were used. These methods are employed to assess the precision, run time, and accuracy of the experimental results. This paper provides the impacts of perturbation-based privacy preserving techniques. It is observed that hybrid approaches are more efficient than the traditional approach

Exploring machine learning techniques for fake profile detection in online social networks

The online social network is the largest network, more than 4 billion users use social media and with its rapid growth, the risk of maintaining the integrity of data has tremendously increased. There are several kinds of security challenges in online social networks (OSNs). Many abominable behaviors try to hack social sites and misuse the data available on these sites. Therefore, protection against such behaviors has become an essential requirement. Though there are many types of security threats in online social networks but, one of the significant threats is the fake profile. Fake profiles are created intentionally with certain motives, and such profiles may be targeted to steal or acquire sensitive information and/or spread rumors on online social networks with specific motives. Fake profiles are primarily used to steal or extract information by means of friendly interaction online and/or misusing online data available on social sites. Thus, fake profile detection in social media networks is attracting the attention of researchers. This paper aims to discuss various machine learning (ML) methods used by researchers for fake profile detection to explore the further possibility of improvising the machine learning models for speedy results

SSDT: Distance Tracking Model Based on Deep Learning

Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and population vulnerability increased all over the world due to lack of effective remedial measures. Nowadays vaccines are available; but in India, only 18.8% population has been fully vaccinated till now. Therefore, social distancing is only precautionary norm to avoid the spreading of this deadly virus. The risk of virus spread can be avoided by adhering to this norm. The main objective of this work is to provide a framework for tracking social distancing violations among people. This paper proposes a deep learning platform-based Smart Social Distancing Tracker (SSDT) model which is trained on MOT (Multiple Object Tracking) datasets. The proposed model is a hybrid approach that is a combination of YOLOv4 as object detection model merged with MF-SORT, Kalman Filter and brute force feature matching technique to distinguish people from background and provide a bounding box around these. Further, the results are also compared with another model, namely, Faster- RCNN in terms of FPS (frames per second), mAP(mean Average Precision) and training time over the dataset. The results show that the proposed model provides better and more balanced results. The experiment has been carried out in challenging conditions including, occlusion and under lighting variations with mAP of 97% and a real-time speed of 24 fps. The datasets provide numerous classes and from all the classes of objects, only people class has been used for identifying people in a closet. The ultimate goal of the model is to provide a tracking solution that will be helpful for different authorities to redesigning the layout of public places and reducing the risk. This model is also helpful in computing the distance between two people in an image and the results confirm that the proposed model successfully distinguishes between individuals who walk too close or breach the social distancing norms