Paper | Focus | Limitations |
---|---|---|
[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 |