A system, method and computer readable memory for determining journalist risk of a dataset using population equivalence class distribution estimation. The dataset may be a cross-sectional data set or a longitudinal dataset. The determine risk of identification can be determined and used in de-identification process of the dataset.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method of determining journalist risk associated with a dataset, the method comprising: retrieving the dataset containing a plurality of records containing personal data, the dataset representing a sample of individuals and data associated with a larger population; determining sample equivalence class (EC) distribution of the dataset; equating a population EC distribution to the determined sample EC distribution; calculating probability that an EC in the dataset of size x came from population of size y for all x and y; and calculating the journalist risk measurement using calculated probability; wherein the equivalence classes define a collection of all records in the dataset containing identical values for all quasi-identifiers in the data. 2. The method of claim 1 wherein the dataset is a cross-sectional dataset. 3. The method of claim 2 further comprising: determining sample EC distribution from the dataset after retrieving the dataset. 4. The method of claim 1 wherein the dataset is a longitudinal dataset. 5. The method of claim 4 wherein prior to equating the population EC distribution to the sample EC distribution, the method further comprising: determining similarity measures for the dataset; rounding the similarity measure is rounded to integer values; determining a number of each similarity measure; and dividing the number of each similarity measure count by the measure to obtain sample EC distribution. 6. The method of claim 1 wherein the probability that an equivalence class of size y in the population will be sampled to an equivalence class of size x in the sample, is calculated: Pr  ( size   x   in   sample | size   y   in   population ) = ( y x )  ( N - y n - x ) / ( N n ) where n is the sample size, N is the population size and ( y x ) = y ! x !  ( y - x ) ! . 7. The method of claim 6 wherein the probability that an equivalence class is of size y in the population and is an equivalence class of size x in the population is: Pr(size x in sample|size y in population)×Pr(y) where Pr(y)=num. of equivalence classes of size y in pop./total number of equivalence classes in population. 8. The method of claim 7 wherein the journalist risk is determined by: Risk journalist = ∑ i = 1 m   ax   size  ∑ j = i ma   x   size  Pr  ( size   j   in   pop . | size   i   in   samp . ) × Prop  ( i ) / j where Prop(i)=number of records in equivalence classes of size i/n. 9. The method of claim 1 wherein the journalist risk is based upon matching someone from the population in the sample wherein the risk is determined by: Risk journalist = J / ∑ i = 1 ma   x   size  ∑ j = i ma   x   size  Pr  ( size   j   in   pop . | size   i   in   samp . ) × EC_num  ( i ) × j where J is the number of equivalence classes in the sample and EC_num(i) is the number of equivalence classes of size i in the sample. 10. The method of claim 1 wherein the determined risk is used in performing de-identification of the dataset. 11. The method of claim 1 wherein a sample and population equivalence class distributions are identical. 12. A system for determining journalist risk associated with a dataset, the system comprising: a memory containing the dataset containing a plurality of records containing personal data, the dataset representing a sample of individuals and data associated with a larger population, dataset having equivalence classes defining a collection of all records in the dataset containing identical values for all quasi-identifiers in the data; and a processor coupled to the memory, the processor executing instructions for: determining sample equivalence class (EC) distribution of the dataset; equating a population EC distribution to the determined sample EC distribution; calculating probability that an EC in the dataset of size x came from population of size y for all x and y; and calculating the journalist risk measurement using calculated probability. 13. The system of claim 12 wherein the dataset is a cross-sectional dataset. 14. The system of claim 13 further comprising: determining sample EC distribution from the dataset after retrieving the dataset. 15. The system of claim 12 wherein the dataset is a longitudinal dataset. 16. The system of claim 15 wherein prior to equating the population EC distribution to the sample EC distribution, the method further comprising: determining similarity measures for the dataset; rounding the similarity measure is rounded to integer values; determining a number of each similarity measure; and dividing the number of each similarity measure count by the measure to obtain sample EC distribution. 17. The system of claim 12 wherein the probability that an equivalence class of size y in the population will be sampled to an equivalence class of size x in the sample, is calculated: Pr  ( size   x   in   sample | size   y   in   population ) = ( y x )  ( N - y n - x ) / ( N n ) where n is the sample size, N is the population size and ( y x ) = y ! x !  ( y - x ) ! . 18. The system of claim 17 wherein the probability that an equivalence class is of size y in the population and is an equivalence class of size x in the population is: Pr(size x in sample|size y in population)×Pr(y) where Pr(y)=num. of equivalence classes of size y in pop./total number of equivalence classes in population. 19. The system of claim 18 wherein the journalist risk is determined by: Risk journalist = ∑ i = 1 ma   x   size  ∑ j = i ma   x   size  Pr  ( size   j   in   pop . | size   i   in   samp . ) × Prop  ( i ) / j where Prop(i) =number of records in equivalence classes of size i/n. 20. The system of claim 12 wherein the journalist risk is based upon matching someone from the population in the sample wherein the risk is determined by: Risk journalist = J / ∑ i = 1 ma   x   size  ∑ j = i ma   x   size  Pr  ( size   j   in   pop . | size   i   in   samp . ) × EC_num  ( i ) × j where J is the number of equivalence classes in the sample and EC_num(i) is the number of equivalence classes of size i in the sample. 21. A non-transitory computer readable memory containing instructions for determining journalist risk associated with a dataset, the instructions which when executed by a processor perform: retrieve the dataset containing a plurality of records containing personal data, the dataset representing a sample of individuals and data associated with a larger population; determine sample equivalence class (EC) distribution of the dataset; equate a population EC distribution to the determined sample EC distribution; calculate probability that an EC in the dataset of size x came from population of size y for all x and y; and calculate the journalist risk measurement using calculated probability; wherein the equivalence classes define a collection of all records in the dataset containing identical values for all quasi-identifiers in the data.