Ensure Hierarchal Identity Based Data Security in Cloud Environment

Kaushik, Shweta; Gandhi, Charu

doi:10.4018/978-1-7998-5351-0.ch022

Cited by 4 publications

(6 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal of action recognition is to detect and recognize human actions automatically in video sequences. Due to the growing demand for automatic interpretation of human action, action recognition has caught the attention in both academia and industry [16]. Analyzing and understanding a person's action in a video is necessary for a wide range of applications, such as web video classification [17], assisting the visually impaired [8], surveillance and security.…”

Section: Action Recognitionmentioning

confidence: 99%

“…So far, the approaches used for action recognition include template matching methods, hidden Markov models (HMM) and machine learning methods [16]. Shechtman et al [18].…”

Section: Action Recognitionmentioning

confidence: 99%

“…propose a human action classifier using masked joint trajectories based on Nearest Neighbor (NN). However, SVM-and NN-based action recognition methods assume that the data distribution in training and testing stages are the same, which is difficult to adapt to realistic scenarios [16]. CNN has been studied extensively in action recognition due to its excellent performance in image processing [10].…”

Section: Action Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Deep action: A mobile action recognition framework using edge offloading

Zhang

Duan

et al. 2021

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

Recording users' lives as short-form videos has been an emerging trend with the advance of mobile devices. The videos contain a wealth of information that requires a significant amount of computation to retrieve. In this paper, we propose Deep action, a framework that leverages edge offloading to enable human actions recognition on mobile devices. Deep action first samples frames from a video according to the accuracy requirement. The sampled frames are then compressed and fed into deep learning models to generate an action label. Considering the varying conditions of the wireless connection, we design an online scheduler to strategically offload compressed video snippets to the edge server. Furthermore, we use OpenCL to implement the video compression-related operations on mobile GPU, such that the model inference and video compression can operate in parallel on the mobile device. We implement Deep action on the Android OS and evaluate it on a commercial off-the-shelf mobile device and an edge server. The performance evaluation demonstrates that Deep action brings up to 19 × and 13 × execution speedup, compared to the local-only and remote-only strategies, respectively.

show abstract

Section: Action Recognitionmentioning

confidence: 99%

“…So far, the approaches used for action recognition include template matching methods, hidden Markov models (HMM) and machine learning methods [16]. Shechtman et al [18].…”

Section: Action Recognitionmentioning

confidence: 99%

Section: Action Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Deep action: A mobile action recognition framework using edge offloading

Zhang

Duan

et al. 2021

Peer-to-Peer Netw. Appl.

View full text Add to dashboard Cite

show abstract

“…A plenty of methods have been proposed to perform authentication [2,[6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Among them, the alphanumerical password authentication is the primary form, because this form is easy to implement.…”

Section: Introductionmentioning

confidence: 99%

Verification Grid and Map Slipping Based Graphical Password against Shoulder-Surfing Attacks

Wang

Liao

Meng

et al. 2022

Security and Communication Networks

View full text Add to dashboard Cite

Graphical password systems have received significant attention as one potential solution to the need for more usable authentication, but graphical passwords are often considered prone to shoulder-surfing attacks. In this paper, in order to build a balance between usability and security for authentication, we propose a new graphical password scheme by combining a specific verification grid with map slipping strategy. In the proposed scheme, a set of fixed grids are pregenerated on a map. During the registration process, the user is not only asked to select several points in sequence on the map to form a password route, but also required to choose one of the pregenerated grids as a specific verification grid for the subsequent authentication. The password route and the specific verification grid together form the complete graphical password. During the authentication process, the user needs to slip the map to let each point on the password route sequentially fit inside the specific verification grid which is already remembered by the user but difficult to be detected by the attackers. With the specific verification grid and the map slipping strategy, the proposed scheme can effectively defend against shoulder-surfing attacks. Meanwhile, the password points are represented as coordinates on the map; therefore, the proposed scheme has a negligible storage burden. The comparative experiments show that, using the proposed scheme, the success rate of shoulder-surfing defense can be increased by 37% to 56% with different grid sizes and password point numbers, and the usability of passwords can also be improved by 3% to 6%. Therefore, the proposed scheme can achieve good shoulder-surfing defense and reasonable usability simultaneously.

show abstract

“…Additionally, the unbalanced cloud servers cause increased power consumption, poor resource utilization, increased response time, and decreased throughput [7]. So, an effective load balancing methodology is required for balancing the load of cloud servers, which improves the overall performance of the cloud servers [8]. In addition to this, the outsourced data are the crucial entities in the cloud computing environment that suffers from numerous attacks and threats by exploring the vulnerabilities available in the cloud modules [9,10].…”

Section: Introductionmentioning

confidence: 99%

Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm

2022

IJIES

View full text Add to dashboard Cite

In recent decades, the rapid growth of cloud computing has led to increasing concerns about the security and energy requirement of cloud data centers. To overcome the concerns of load balancing and data security, a new hybrid model is proposed in this article. Firstly, a Modified Particle Swarm Optimization (MPSO) technique is proposed for balancing the tasks between heavy-loaded Virtual Machine (VMs) and light-loaded VMs. An effective load balancing improves the performance and availability of website, database, application and other computing resources. In the MPSO, a linear decreasing inertia weight is included to achieve optimal solutions in both VM migrations and load balancing. Secondly, an Enhanced Elliptic Curve Cryptography (EECC) algorithm is proposed for effective data security. In the proposed EECC algorithm, a new pseudo-random key is combined with a public key to improve the security of cloud data centers. In this work, the effectiveness of the proposed MPSO-EECC model is validated in terms of Service Level Agreement (SLA), execution time, energy consumption, energy SLA violation, encryption, and decryption comparison time. As represented in the experimental section, the proposed MPSO technique almost reduced 30%-70% of energy consumption compared to the existing optimization techniques. Similarly, the proposed EECC algorithm reduced 10ms to 30ms of decryption comparison time related to the comparative cryptography algorithms.

show abstract

Ensure Hierarchal Identity Based Data Security in Cloud Environment

Cited by 4 publications

References 20 publications

Deep action: A mobile action recognition framework using edge offloading

Deep action: A mobile action recognition framework using edge offloading

Verification Grid and Map Slipping Based Graphical Password against Shoulder-Surfing Attacks

Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm

Contact Info

Product

Resources

About