Are you looking to add Data De-identification and Pseudonymity to your arsenal of tools? Maybe for your business or personal use only, whatever it is – it’s always a good idea to know more about the most important Data De-identification and Pseudonymity statistics of 2024.
My team and I scanned the entire web and collected all the most useful Data De-identification and Pseudonymity stats on this page. You don’t need to check any other resource on the web for any Data De-identification and Pseudonymity statistics. All are here only 🙂
How much of an impact will Data De-identification and Pseudonymity have on your day-to-day? or the day-to-day of your business? Should you invest in Data De-identification and Pseudonymity? We will answer all your Data De-identification and Pseudonymity related questions here.
Please read the page carefully and don’t miss any word. 🙂
On this page, you’ll learn about the following:
Best Data De-identification and Pseudonymity Statistics
☰ Use “CTRL+F” to quickly find statistics. There are total 35 Data De-identification and Pseudonymity Statistics on this page 🙂Data De-identification and Pseudonymity Latest Statistics
- 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]
I know you want to use Data De-identification and Pseudonymity Software, thus we made this list of best Data De-identification and Pseudonymity Software. We also wrote about how to learn Data De-identification and Pseudonymity Software and how to install Data De-identification and Pseudonymity Software. Recently we wrote how to uninstall Data De-identification and Pseudonymity Software for newbie users. Don’t forgot to check latest Data De-identification and Pseudonymity statistics of 2024.
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 is the process of removing all personally identifiable information from a dataset, making it anonymous. This method is commonly used when sharing data for research purposes or other forms of analysis. By de-identifying data, organizations can protect individuals’ personal information while still allowing researchers to draw meaningful insights from the information.
Pseudonymity, on the other hand, involves replacing individuals’ real names with pseudonyms or codes. This method allows for the tracking of individuals’ activities across different datasets without revealing their true identity. Pseudonymity can be useful in cases where it is necessary to link data points together without disclosing personal information.
While data de-identification and pseudonymity are important steps in safeguarding privacy, they are not foolproof. There are several limitations and challenges associated with these methods that need to be addressed.
One major limitation of data de-identification is the risk of re-identification. Even if all personally identifiable information is removed from a dataset, it may still be possible to re-identify individuals by cross-referencing the de-identified data with other sources of information. As technology advances, re-identification techniques become more sophisticated, posing a significant threat to individuals’ privacy.
Similarly, pseudonymity is not a perfect solution to anonymity. Pseudonyms can often be reverse-engineered or linked back to individuals through various means. Additionally, pseudonyms can be compromised if they are used across multiple datasets, allowing for correlation and potential re-identification.
Moreover, data de-identification and pseudonymity do not provide absolute protection against unauthorized access or misuse of data. While these methods can help mitigate risks, they cannot entirely eliminate the possibility of data breaches or malicious intent. Organizations must implement robust security measures and protocols to safeguard de-identified and pseudonymous data effectively.
In conclusion, data de-identification and pseudonymity are valuable tools in protecting individuals’ privacy and ensuring anonymity in data sharing and analysis. However, these methods are not without their limitations and challenges. It is essential for organizations to balance the benefits of de-identification and pseudonymity with the potential risks and vulnerabilities associated with these techniques. By continuously improving and evolving these methods, we can strive to maintain a delicate balance between data protection and data utility in an increasingly digital world.
In Conclusion
Be it Data De-identification and Pseudonymity benefits statistics, Data De-identification and Pseudonymity usage statistics, Data De-identification and Pseudonymity productivity statistics, Data De-identification and Pseudonymity adoption statistics, Data De-identification and Pseudonymity roi statistics, Data De-identification and Pseudonymity market statistics, statistics on use of Data De-identification and Pseudonymity, Data De-identification and Pseudonymity analytics statistics, statistics of companies that use Data De-identification and Pseudonymity, statistics small businesses using Data De-identification and Pseudonymity, top Data De-identification and Pseudonymity systems usa statistics, Data De-identification and Pseudonymity software market statistics, statistics dissatisfied with Data De-identification and Pseudonymity, statistics of businesses using Data De-identification and Pseudonymity, Data De-identification and Pseudonymity key statistics, Data De-identification and Pseudonymity systems statistics, nonprofit Data De-identification and Pseudonymity statistics, Data De-identification and Pseudonymity failure statistics, top Data De-identification and Pseudonymity statistics, best Data De-identification and Pseudonymity statistics, Data De-identification and Pseudonymity statistics small business, Data De-identification and Pseudonymity statistics 2024, Data De-identification and Pseudonymity statistics 2021, Data De-identification and Pseudonymity statistics 2024 you will find all from this page. 🙂
We tried our best to provide all the Data De-identification and Pseudonymity statistics on this page. Please comment below and share your opinion if we missed any Data De-identification and Pseudonymity statistics.