Toward Secure Data Computation and Outsource for Multi-User Cloud-Based IoT
ABSTARCT :
Cloud computing has promoted the success of Internet of Things (IoT) with offering abundant storage and computation resources where the data from IoT sensors can be remotely outsourced to the cloud servers, whereas storing, exchanging and processing data collected through IoT sensors via centralised or decentralised cloud servers make cloud-based IoT systems prone to internal or external attacks. To protect IoT data against potential malicious users and adversaries, some cryptographic schemes have been applied to ensure confidentiality and integrity of IoT data. It is however a challenging task to perform any arithmetical computations once data items are encrypted. Fully-homomorphic encryption which is based on lattices can, in principle, provide a solution, but it is unfortunately inefficient in computation and hence cannot be applied to IoT. Fully-homomorphic encryption is feasible when we allow an involvement of semi-trusted server. However, it is challenging to provide such a system in the situation of distributed environments for shared IoT data. We solve this problem and provide a fully-homomorphic encryption scheme for cloud-based IoT applications. We introduce a new method with the aid of semi-trusted server who can help in the computation of the homomorphic multiplications without gaining any useful information of the encrypted data.
EXISTING SYSTEM :
? Our scheme is on par with the plaintext practice in terms of the deduplication performance while gaining better security guarantees compared to the existing work.
? Majority of the existing SE schemes, including our previous work, are software-based solutions built on top of diverse cryptographic primitives, which result in a rich set of secure search indexes and algorithm designs.
? However, the existing secure deduplication designs to some extent, are at odds with the real-world dedupe requirements in terms of security and performance.
? This gives us the desired asymmetry between security and performance, i.e. resilient to multiple compromised clients, compared to existing work, but only with minimal dedupe performance loss.
DISADVANTAGE :
? Although cloud computing has fulfilled most of the demands of modern technology, it may not be a suitable solution as there are still unresolved problems, whereas IoT devices and applications need to be processed swiftly.
? As devices can always breakdown or become vulnerable to malicious attacks, authentication alone is not adequate to fix these problems.
? By having this problem, it becomes a challenging task to deploy secure communication protocols and encryption–decryption methods amongst Fog nodes and IoT devices.
? However, it is a problematic approach because this approach provides only limited support to make an evaluation and the quality of the audit heavily depends on auditor’s knowledge and experience.
PROPOSED SYSTEM :
• The proposed SE scheme enables users to freely update the secure index and the corresponding file collection.
• The proposed scheme incurs minimal ciphertext size expansion and computation overhead.
• The security of the proposed scheme is derived from the MSSE security against adaptive chosen-keyword attacks.
• The proposed system supports a rich set of IR functions and query types while ensuring the confidentiality and integrity of the query process.
• We confine the proposed security and privacy preservation design to the enterprise internal network via setting up a key server in order to stay transparent to and compatible with the existing public cloud backup service.
ADVANTAGE :
? Security and performance are both highly required in terms of different contextual devices and applications.
? However, Cloud Watcher is unable to generate routing path and, if there many new flows in the network path, it is less efficient and performance degrades.
? A rouge Fog device also known as a malicious Fog device can send illegal data and run over the entire network, which can have undesirable influences on the entire network performance and amplify the packet loss.
? Therefore, they need to use different authentication methods for different services where the performance of the authentication methods is different in the context of latency, efficiency and scalability.
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