Murat Kantarcioglu, Umid Suleymanov, Ilhama Novruzova, Khalid Mammadov, Natavan Hasanova
We lifted 12 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jjgold012/lab-project-fairness | — | 1 of 12 |
| Function | Status | Where it lives |
|---|---|---|
| logistic | Ran | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("36a1468d01fa52b5") |
| make_output_dir | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("1156209c2588ddeb") |
| measure_objective_results | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("29a935537864cec9") |
| measure_relaxed_results | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("9d5c2c4e42cee3f1") |
| plot_results | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("74be7e92a19ceaf3") |
| plot_theta | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("7f96df2d218e1f2a") |
| show_results | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("c520a2d7f675fe84") |
| show_summary | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("68d216c70e46d1b5") |
| show_theta | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("c1ae26fe07192ba6") |
| solve_convex | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("13e4d76bb3f9c6d2") |
| solve_one_time_by_type | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("72ed3432d42a637d") |
| solve_problem_for_fairness | Not yet run | jjgold012/lab-project-fairness/fairness_project/solver.py pointer only (licence: NONE) · get_code("3e2205bc7114cf82") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Machine learning (ML) models deployed in sensitive domains such as healthcare, law enforcement, and finance must satisfy not only utility requirements but also fairness and privacy guarantees. While prior work has largely examined how privacy-preserving techniques affect fairness, the inverse question-how fairnessenhancing algorithms influence privacy leakage-remains underexplored. We present the first comprehensive study of how fairness interventions affect membership inference privacy risks at the subpopulation level. By adapting the Likelihood Ratio Attack (LiRA) for subgroup auditing, we uncover privacy disparities that aggregate evaluations obscure. We further analyze how Differential Privacy (DP) interacts with fairness-enhancing methods across different categories, showing that DP's privacy benefits and utility costs are unevenly distributed across subpopulations. Our results demonstrate that fairness interventions do not uniformly increase privacy risk; their impact depends on model architecture, subgroup size, and mitigation strategy. These findings reveal that fairness, privacy, and utility must be jointly evaluated at the subpopulation level, and we introduce the first unified empirical framework to support such auditing in practice.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2607.14607")
get_code_for_paper("2607.14607")
have("2607.14607")
Connect an agent — have() is free.