Create a class which constructs an adjacency matrix representation of a graph and performs a few graph operations.

Write an Adjacency Matrix Graph class which has the following:

Two constructors:

Default which makes the matrix of a pre-defined size

Parameterized which takes in a non-negative or 0 size and creates an empty matrix

addEdge: this method returns nothing and takes in two string parameters and a weight. The two integer parameters correspond to the names of vertices and the weight is the weight of the newly created edge.

printDFS: This prints out the vertices names in depth first order. You can find the algorithm the presentation provided.

printBFS: This prints out the vertices names in breadth first order. Like above you can find the algorithm in the presentation.

printDFSForAll: For every vertex in the graph it will print its DFS

printBFSForAll: For every vertex in the graph it will print its BFS

Write another file that tests each of the methods for this graph.

3. The following medical record dataset has been published. SSN Name Race DateOfBirth Sex ZIP MaritalStatusHealthProblem -asian09/27/64 female 94139divorced -asian 09/30/64 female 94139divorced -asian 04/18/64 male 94139married -asian 04/15/64 male 94139married black 03/13/63male 94138 married hypertension obesity chest pairn obesity hypertension black 03/18/63 male 94138married shortness of breath black09/13/64 female 94141 married shortness of breath black09/07/64 female 94141 married -white 05/14/61 male 94138 single -white 05/08/61male 94138single obesity chest pairn obesity -white 09/15/61female 94142 idowed shortness of breath (a) Explain what kind of anonymization has been performed on the released dataset, if any? (b) Considering the following publicly avilable voter registration list, explain if/how it is possible to infer any privacy-sensitive information from the above released data. (5pt.) Name Address City ZIP DOB Sex Party Sue J. Carlson 900 Market St. San Francisco 94142 9/15/61 female |democrat (c) Given attributes Race, DOB, and Sex together as the quasi-identifier, and attribute Health Problem as the sensitive attribute, create and report a 2-anonymous (SkS-anonymit;y where k- 2) version of the medical dataset using generalization approach. (10pt.) (d) Repeat the previous step for attributes Sex, ZIP, and MaritalStatus as quasi-identifier with k-3. Explain what the 1 value of the result is in terms of the $1$-diversity principle (considering distinct l-diversity)? (10pt.) Note: There is no unique answer to this question. However, your result should have fairly low information loss