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Best Data De-identification and Pseudonymity Statistics
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- As of the publication of this guidance, the information can be extracted from the detailed tables of the “Census 2000 Summary File 1 100. [0]
- According to section 164.514 of the HIPAA Privacy Rule, “healthinformation that does not identify an individual and with respect. [1]
- According to the 18th item in Safe Harbor , “anyunique identifying number, characteristic, or code” must beremoved from the data set; otherwise it would be considered personalhealth information. [1]
- In an earlier opinion the Article29 Data Protection Working Party emphasized theimportance of “likely reasonable” in the definition ofidentifiable information in the 95/46/EC Directive. [1]
- Data for 2010 show that 19 percent of health care organizationssuffered a data breach within the previous year ; data for 2012 showthat this number rose to 27 percent. [1]
- At thesame time, it should be recognized that, even when such information wasmade available, the attack reported in Haggie was successful 12 percent ofthe time and unsuccessful 5 percent of the time. [1]
- Articles 32, 33 and 34 as a security measure helping to make data breaches “unlikely to result in a risk to the rights and freedoms of natural persons” thereby reducing liability and notification obligations for data breaches. [2]
- Using our model, we find that 99.98% of Americans would be correctly re identified in any dataset using 15 demographic attributes. [3]
- They argue that this provides strong plausible deniability to participants and reduce the risks, making such de identified datasets anonymous including according to GDPR 32,33,34,35. [3]
- Once trained, our model allows us to predict whether the reidentification of an individual is correct with an average false discovery rate of <6.7% for a 95% threshold. [3]
- p\right)^{n }x, x, …, xn) and the estimated uniqueness \widehat {\Xi. [3]
- a We compare, for each population, empirical and estimated population uniqueness for each population, with 100 independent trials per population). [3]
- For example, date of birth, location , marital status, and gender uniquely identify 78.7% of the 3 million people in this population that our model estimates to be 78.2 ± 0.5%. [3]
- b Absolute error when estimating USA’s population uniqueness when the disclosed dataset is randomly sampled from 10% to 0.1%. [3]
- The boxplots show the distribution of mean absolute error for population uniqueness, at one subsampling fraction across all USA populations. [3]
- p \times np = 0.1% or 3061 records). [3]
- For instance, our model achieves an MAE of 0.029 ± 0.015 when the dataset only contains 1% of the USA population and an MAE of 0.041 ± 0.053 on average across every corpus. [3]
- 2a shows that, when trained on 1% of the USA populations, our model predicts very well individual uniqueness, achieving a mean AUC ) of 0.89. [3]
- For each population, to avoid overfitting, we train the model on a single 1% sample, then select 1000 records, independent from the training sample, to test the model. [3]
- ROC curves for the other populations are available in Supplementary Fig. 3 and have overall a mean AUC of 0.93 and mean false discovery rate of 6.67% for \widehat {\xi _{\boldsymbol{x}}}\, > \, 0.951). [3]
- Our model outperforms by 39% the best theoretically achievable prediction using population uniqueness across every corpus. [3]
- A red point shows the Brier Score obtained by our model, when trained on a 1% sample. [3]
- We train our model on the 5% Public Use Microdata Sample files using ZIP code, date of birth, and gender and validate it using the last national estimate 25. [3]
- Table 1 and Supplementary Note 7 show that, at very small sampling fraction (below 0.1%). [3]
- Incorporating exogenous information reduces, e.g., the mean MAE of uniqueness across all corpora by 48.6% for a 0.1% sample. [3]
- In practice, we estimate the expected mutual information between {\cal{D}}_i For n individuals x, x, …, xn)X, the uniqueness Ξ is the expected percentage of unique individuals. [3]
- x The USA corpus, extracted from the 1 Percent Public Use Microdata Sample files, is available at https//www.census.gov/main/www/pums.html. [3]
- The 5% PUMS files used to estimate the correctness of Governor Weld’s re identification are also available at the same address. [3]
- As a well known study shows, it’s possible to personally identify 87 percent of the U.S. population based on just three data points fivedigit ZIP code, gender, and dateof. [4]
- With both technical and administrative protections, the probability of re identifying data is thus one percent of one percent, or one in 10,000. [5]
- That is, does 10% of your data have a low k value or does 90% of your data have a low k value?. [6]
- In the example above, we see that 100% of the data maps to fewer than 10 people. [6]
- To fix this, without dropping 100% of rows, we applied generalization to convert ages to age ranges. [6]
- Here is the graph after the transform Now only 3.9% of the rows and 21.15% of the unique values fall below the k=10 threshold. [6]
- So as a result, we reduced the re identifiability while preserving much of the data utility, dropping only 3.9% of rows. [6]
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Reference
- hhs – https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html.
- nih – https://www.ncbi.nlm.nih.gov/books/NBK285994/.
- wikipedia – https://en.wikipedia.org/wiki/Pseudonymization.
- nature – https://www.nature.com/articles/s41467-019-10933-3.
- iapp – https://iapp.org/news/a/looking-to-comply-with-gdpr-heres-a-primer-on-anonymization-and-pseudonymization/.
- stanfordlawreview – https://www.stanfordlawreview.org/online/privacy-and-big-data-public-vs-nonpublic-data/.
- google – https://cloud.google.com/blog/products/identity-security/taking-charge-of-your-data-understanding-re-identification-risk-and-quasi-identifiers-with-cloud-dlp.
How Useful is Data De Identification and Pseudonymity
Data de-identification involves the removal or obfuscation of identifiable information from a dataset, such as names, addresses, or social security numbers, in order to preserve confidentiality. Pseudonymity, on the other hand, allows individuals to be identified by a pseudonym or alias rather than their real name. While these approaches can be effective in protecting privacy, they are not foolproof and have their limitations.
One of the main challenges with data de-identification is the risk of re-identification. Even when identifiable information is removed from a dataset, there may still be enough unique combinations of remaining data points to allow for individuals to be re-identified. Advances in data analytics and machine learning have made it increasingly easier to piece together seemingly anonymous data and uncover the identities of individuals. For example, studies have shown that even seemingly innocent information such as dates of birth, zip codes, and gender can be used to re-identify individuals with a high degree of accuracy.
Similarly, pseudonymity can also be circumvented if sufficient additional information is available. For instance, if a pseudonymous user engages in a transaction that can be linked to their real identity through other means, their privacy can be compromised. Additionally, there is always a risk of accidental disclosure of a user’s real identity, such as through human error or system vulnerabilities.
Despite these challenges, data de-identification and pseudonymity still play a crucial role in privacy protection and data security. These methods can significantly reduce the risk of re-identifying individuals and help mitigate the potential harm of data breaches. They also enable greater data sharing and collaboration while maintaining a level of confidentiality.
However, it is important to note that data de-identification and pseudonymity are not silver bullets for protecting privacy. They should be used in conjunction with other privacy-enhancing technologies and practices, such as encryption, access controls, and data minimization. Organizations should also be transparent about their data handling practices and seek consent from individuals whenever possible.
In conclusion, while data de-identification and pseudonymity are valuable tools for safeguarding personal information, they are not foolproof and should be part of a broader privacy protection strategy. As data privacy concerns continue to grow, it is essential for individuals, businesses, and policymakers to stay vigilant and continuously assess and improve their data protection measures. Only through a comprehensive and holistic approach to data privacy can we ensure that individuals’ information remains secure in the digital age.
In Conclusion
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