Secure cloud storage with data dynamic using secure network coding
ABSTARCT :
In the age of cloud computing, cloud users with limited storage can outsource their data to remote servers. These servers, in lieu of monetary benefits, offer retrievability of their clients’ data at any point of time.
Secure cloud storage protocols enable a client to check integrity of outsourced data. In this article, we explore the possibility of constructing a secure cloud storage for dynamic data by leveraging the algorithms involved in secure network coding.
We show that some of the secure network coding schemes can be used to construct efficient secure cloud storage protocols for dynamic data, and we construct such a protocol (DSCS I) based on a secure network coding protocol. ance.
To the best of our knowledge, DSCS I is the first secure cloud storage protocol for dynamic data constructed using secure network coding techniques which is secure in the standard model.
Although generic dynamic data support arbitrary insertions, deletions and modifications, append-only data find numerous applications in the real world.
We construct another secure cloud storage protocol (DSCS II) specific to append-only data — that overcomes some limitations of DSCS I. Finally, we provide prototype implementations for DSCS I and DSCS II in order to evaluate their perform
EXISTING SYSTEM :
Most of these existing works show how RL has been effectively applied to MEC based networks due to network dynamics.
Random task generation and arrival exist for a mobile device since independent tasks are possibly sensed, collected, and generated by multiple types of applications stochastically.
The existing DRL algorithms in terms of convergence, stability, and robustness, proposed a multiagent DRL-based cooperative computation offloading policy in the NOMA enabled MEC system with the aid of expert strategies, scatter networks, and hierarchical agents.
DISADVANTAGE :
The slow convergence caused by high-dimensional action space remains an urgent and challenging problem.
In this paper, we study the computation offloading problem for both mining tasks and data processing tasks in multi-hop multi-user blockchain-empowered MEC.
In our performance optimization problem, it is important to note that costs such as energy consumption and purchasing VMs should be paid immediately.
PROPOSED SYSTEM :
To minimize the expected long-term cost of task delay and dropping penalty, the optimal offloading decision making strategy was proposed in with the aid of long short-term memory (LSTM), dueling DQN, and double DQN algorithms.
In, a low-complexity DRL-based task execution time and energy consumption optimization is proposed to avoid frequent offloading decisions and resource allocation recalculations, considering time-varying wireless channels and dynamic edge computational resources.
A context-aware attention mechanism is proposed for the online weight adjustment of each action value.
ADVANTAGE :
The performance of the algorithm is greatly affected by the convergence and accuracy of the deep Q-network (DQN).
In this way, we can make the most of the evaluating role of critic network in exploration, avoiding useless exploration to a great extent without reducing the performance.
A resource allocation algorithm based on deep Qlearning is proposed into optimize the performance of computation offloading in MEC.
All these algorithms are constrained by the trade-off between efficiency and optimality.
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