Privacy Invasion of individuals in the digital era is one of the major threats. The massive growth of data about individuals has increased the probability of privacy theft identi?cation. There are many existing solutions available to overcome Individual identi?cation threat. But none of these guaran
Semantic Privacy Preserving for Big Data
Privacy Invasion of individuals in the digital era is one of the major threats. The massive growth of data about individuals has increased the probability of privacy theft identi?cation. There are many existing solutions available to overcome Individual identi?cation threat. But none of these guarantee a justi?ed solution in terms of Privacy versus utility. Especially in Medical ?eld special care needs to be taken while doing sensitization or anonymization on the original dataset. On the other hand, any published data with its high utility helps to improve existing research because our basic motivation is that no one can identify individuals from the published dataset and the utility of data remains high. To overcome this problem, we proposed Semantic Hybrid Privacy Framework (Range Random Sampling + DAD). The basic goal of this project is to create such a tool which can be used by public sectors to publish data without hesitation of identity theft and complete privacy so that researchers can use this data for their own benefit. We also present how our hybrid framework tackles with existing Attacks and provide semantic privacy.
An application based software tool that increases the security of the data set. The tool will be dealing with huge data sets, that is why the equipment required for that will be necessary in order for its completion
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| SSD Drive 1TB | Equipment | 1 | 35000 | 35000 |
| External HDD 1 Tb | Equipment | 1 | 10000 | 10000 |
| GPU 2GB | Equipment | 1 | 20000 | 20000 |
| Total in (Rs) | 65000 |
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