Let's solve each part step by step as you requested. --- ### **Step 1: Introduction to the Concept** **k-anonymity** is a principle in data privacy that ensures that for each combination of quasi-identifier values (attributes that could potentially identify an individual), there are at least **k** records sharing those same values in the dataset. This makes it difficult to uniquely identify any individual based on those quasi-identifiers. In this context, *k = 3* means that every unique combination of quasi-identifier values must appear in at least 3 records. --- ### **Step 2: Answer to Part (a)** **(a) Explanation for k-anonymity (k = 3) in this context:** For this dataset to satisfy k-anonymity with k = 3, it means that for every possible combination of the values of the quasi-identifier attributes (Age, Gender, Zip Code, Number of Visits), there must be at least 3 patients who share exactly those values. This ensures that an individual's data cannot be distinguished from at least 2 other individuals, protecting their privacy. --- ### **Step 3: Answer to Part (b)** **(b) Identify the quasi-identifiers in the provided dataset:** The quasi-identifiers in the provided dataset are: - **Age** - **Gender** - **Zip Code** - **Number of Visits** These are the attributes that, in combination, could potentially be used to re-identify a patient. --- ### **Step 4: Answer to Part (c)** **(c) Does the raw dataset satisfy k-anonymity (k = 3)?** No, the raw dataset does **not** satisfy 3-anonymity. For example, the first 3 records (Patient IDs 1, 2, 3) share the same Age and Zip Code, but differ in Gender and Number of Visits, making their combinations unique. There are also combinations of quasi-identifiers (like Age=42, Gender=Female, Zip=10473, Visits=1) that appear only once. Thus, there are combinations that do not appear at least 3 times. --- ### **Step 5: Answer to Part (d)** **(d) Modify the table to satisfy 3-anonymity:** To achieve 3-anonymity, we can generalize (replace with broader categories) or suppress some values in the quasi-identifiers to ensure each combination appears at least 3 times. Here's one way to do it: | Patient ID | Age | Gender | Zip Code | Number of Visits | Diagnosis | |------------|------|--------|----------|------------------|------------| | 1 | 3* | * | 1047* | * | Flu | | 2 | 3* | * | 1047* | * | Cold | | 3 | 3* | * | 1047* | * | Fever | | 4 | 4* | * | 1047* | * | Headache | | 5 | 4* | * | 1047* | * | Sprain | | 6 | 4* | * | 1047* | * | Rash | | 7 | 2* | * | 1047* | * | Fracture | | 8 | 2* | * | 1047* | * | Allergies | | 9 | 2* | * | 1047* | * | Infection | | 10 | 2* | * | 1047* | * | Injury | - Here, * means the value is generalized (e.g., Age 3* means 30-39, 4* means 40-49, 2* means 20-29; Zip 1047* means all zip codes starting with 1047; Gender and Number of Visits are suppressed). - Now, for each quasi-identifier group, there are at least 3 records. --- ### **Step 6: Final Short Summary** To satisfy 3-anonymity, we generalized the quasi-identifiers so that each combination appears in at least three records, thus protecting individual privacy. --- ### **Step 7: One-Sentence Step Summary** - **Step 1:** Introduced k-anonymity and its requirement. - **Step 2:** Explained what k-anonymity means in this context. - **Step 3:** Listed the quasi-identifiers in the dataset. - **Step 4:** Checked if the original dataset satisfies 3-anonymity (it doesn't). - **Step 5:** Modified the dataset to achieve 3-anonymity. - **Step 6:** Summarized the final answer in very few sentences.