PRIVACY-PRESERVING TECHNIQUES FOR CLOUD DATA PROCESSING

As digital technology advances, the increasing use of connected devices presents both challenges and opportunities. In the cloud computing environment, the privacy of electronic data is a serious issue that involves extra precautions for sensitive data stored, processed, and managed in cloud environments.

Some of the key privacy-preserving Techniques include:

1. Encryption and tokenization

Encryption and tokenization techniques are critical for securing data both at rest and in transit. File-level encryption protects data before it is uploaded to the cloud. (Altilar, 2014) proposed a strategy for normalizing the histogram of the feature tables. The normalized table's entities are then encrypted using homomorphic encryption. This method stops the malicious cloud from making an informed guess about the inputs and protects against analytical attacks such as frequency analysis attacks.

Implementing "sharding" can increase cloud data security by breaking down data into fragments and storing them in numerous locations, making it difficult for hostile actors to reconstruct the complete file.

2. Intrusion detection and response

Intrusion detection and response systems detect and mitigate harmful behavior in cloud settings. 

To monitor network traffic for suspicious activity, you can use signature-based and anomaly-based intrusion detection technologies. Intrusion response entails addressing an attack to adhere to the security policy and minimize any damage.

3. Auditing and monitoring

Regularly reviewing and updating policies and procedures can assist organizations in monitoring data usage anonymously, tracking provenance, and maintaining data confidentiality. Companies should also consider using automated technologies to monitor cloud environments for modifications or unwanted access.

4. Anonymization approach for privacy-preserving

L. Badger (2012) introduced a new anonymity method for cloud computing services that anonymizes data before providing it to service providers by removing or altering identifiable information from datasets. As a result, in interconnected cloud computing settings, the clouds can 

collaborate to readily re-identify the original data, which helps to safeguard it from identity revelation. It employs K-anonymity, which guarantees that each record in a dataset is indistinguishable from at least k-1 other records.

5. Privacy by ranking

In situations when data needs to be shared while maintaining anonymity, ranking-based systems can be effective. It employs Top-k searches to return the k highest-ranked records while retaining the whole dataset. Rank aggregation aggregates rank from many sources while protecting anonymity.

6. Privacy by Probability:

This method includes introducing randomness into the data. Techniques such as differential privacy introduce noise into query results, making it more difficult to deduce specific information about individuals. It strikes a reasonable compromise between data utility and privacy.

(Weizhong Qiang, 2015) developed a multi-level recovery technique that incorporates process-level recovery, virtual machine recreation, and host rescheduling, allowing for comprehensive 

and efficient fault tolerance across various cloud components.

7. Privacy-preserved access control

(al., 2011) proposed a new approach, the encryption system, that offers flexible and fine-grained access control on outsourced data in cloud environments. This ensures privacy by enlarging data to include all possible input regions. For this reason, the scheme was divided into two phases: base and surface.

In the first phase, the data owner encrypts the data files using a policy-hidden attribute-set-

based encryption method, assigning each data file an access structure from which distinct attribute sets are formed. The attribute sets are used to produce privileged users' private keys, allowing them to decrypt data files downloaded from the cloud server. 

The second phase, on the other hand, is completed by cloud providers following the data owner's initiation launch. This phase implements the Server Re-encryption Mechanism (SRM). In the SRM, the cloud server re-encrypts data files upon receipt of request messages from the data owner, without requiring the disclosure of file content and user information, as well as the updating of decryption keys for re-encrypted data files. This task is transferred to the cloud server rather than the data owner due to customers' dynamic join and leave, high availability, extensive storage space, and compute capability of cloud servers.