iam working in searching encrypted data, then retrieving from this data. i want to compute precision and recall so i want to know the formula to compute true positive , false positive , true negative and false negative in this case of searching and retrieving from encrypted data.
Near to zero calculation is ofcourse difficult. It is dependent on how much accuracy you needed ie upto how much precision value of accuracy to be considered. Therefore it's very hard to say a point is above or below a line. Computational cost increases with increasing accuracy.
Indeed it is my first time to heard of what you are talking about. Although it's sounds interesting and useful.
Best luck
Hope these articles will clear your questions
Article Robust Encryption
The following link may be useful to you.
https://en.wikipedia.org/wiki/Sensitivity_and_specificity
Strongly endorsed the recommended link of Balaji, which in conclusion says:
True Positive: Sensitivity (also called the true positive rate, the recall, or probability of detection in some fields) measures the proportion of actual positives that are correctly identified as such (e.g., the percentage of sick people who are correctly identified as having the condition).
True Negative: Specificity (also called the true negative rate) measures the proportion of actual negatives that are correctly identified as such (e.g., the percentage of healthy people who are correctly identified as not having the condition).
Wikipedia provides the following definitions:
1. True positive rate (or sensitivity): TPR=TP/(TP+FN)TPR=TP/(TP+FN)
2. False positive rate: FPR=FP/(FP+TN)FPR=FP/(FP+TN)
3. True negative rate (or specificity): TNR=TN/(FP+TN)TNR=TN/(FP+TN)
Condition: A Not A Test says “A” True positive | False positive ---------------------------------- Test says “Not A” False negative | True negative
In this table, “true positive”, “false negative”, “false positive” and “true negative” are events (or their probability). What you have is therefore probably a true positive rate and a false negative rate. The distinction matters because it emphasizes that both numbers have a numerator and a denominator.
Where things get a bit confusing is that you can find several definitions of “false positive rate” and “false negative rate”, with different denominators.
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for the definition of true/false positive/negative rates they have been given above
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now, what, exactly, has been encrypted ?
if the class information has been encrypted and if the system returns the encrypted class information, there is no hope to compute anything meaningful without prior information
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if this information is the capacity to decrypt, then the problem is trivial ...
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... but there is one interesting case where you can indeed compute the rates on the encrypted class information and recover the correct result : homomorphic encryption
https://en.wikipedia.org/wiki/Homomorphic_encryption
(you can even compute anything you want on the encrypted data, at least in principle)
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is this what this question is about ?
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