Table 5 Summary on recent approaches used in big data privacy

[56]

Discusses experiences and issues encountered when successfully combined anonymization, privacy protection, and Big data techniques to analyze usage data while protecting the identities of users

It still uses K-anonymity technique which is vulnerable to correlation attack

[61]

Proposed the privacy preserving data mining techniques in Hadoop, i.e. solve privacy violation without utility degradation

Its execution time is affected by noise size

[67]

Introduced an efficient and privacy-preserving cosine similarity computing protocol

Need significant research efforts for addressing unique privacy issues in some specific big data analytics

[68]

Discussed and suggested how an existing approach “differential privacy” is suitable for big data

This method depends totally on calculation of the amount of noise by the curator. So, if curator is compromised the whole system fails

[69]

Proposed a scalable two-phase top-down specialization (TDS) approach to anonymize large-scale data sets using the MapReduce framework on cloud

It uses anonymization technique which is vulnerable to correlation attack

[70]

Proposed various privacy issues dealing with big data applications

Customer segmentation and profiling can easily lead to discrimination based on age gender, ethnic background, health condition, social, background, and so on

[71]

Proposed an anonymization algorithm (FAST) to speed up anonymization of big data streams

Further research required to design and implement FAST in a distributed cloud-based framework in order to gain cloud computation power and achieve high scalability

[72]

The novel framework proposed into achieve privacy-preserving machine learning

The training data are distributed and each shared data portion of large volume, is not able to achieve distributed feature selection

[73]

Proposed methodology provides data confidentiality, secure data sharing without Re-encryption and access control for malicious insiders and forward and backward access control

Limiting the trust level in the cryptographic server