We lifted 11 functions out of this paper's own repositories and ran 8 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 |
|---|---|---|
| pps-lab/cohere | canonical | 8 of 11 |
| Function | Status | Where it lives |
|---|---|---|
| adapt_allocation_algo | Ran | pps-lab/cohere/doe-suite-config/does_etl_custom/etl/sp.py code served (permissive licence) · get_code("66ef59fd47275597") |
| calc_n_possible | Ran | pps-lab/cohere/workload-simulator/workload_simulator/report.py code served (permissive licence) · get_code("48db0dd1d306e5ff") |
| convert_to_rdp | Ran | pps-lab/cohere/workload-simulator/workload_simulator/report.py code served (permissive licence) · get_code("f1977241507ea326") |
| extract | Ran | pps-lab/cohere/workload-simulator/workload_simulator/report.py code served (permissive licence) · get_code("308d14b79865d045") |
| get_active_blocks | Ran | pps-lab/cohere/hyperparam-explorer/hyperparam_explorer/hyperparameter/accounting_info.py code served (permissive licence) · get_code("310239cc337c9cb9") |
| get_unlocked_budget | Ran | pps-lab/cohere/hyperparam-explorer/hyperparam_explorer/hyperparameter/accounting_info.py code served (permissive licence) · get_code("0b7f3ff8c27066cd") |
| lighten_color | Ran | pps-lab/cohere/doe-suite-config/does_etl_custom/plots/utility_plot_sp.py code served (permissive licence) · get_code("79562521f7728505") |
| make_filled_circle | Ran | pps-lab/cohere/doe-suite-config/does_etl_custom/plots/sprites.py code served (permissive licence) · get_code("fc77d2f5043865c5") |
| build_round_request_summary | Not yet run | pps-lab/cohere/doe-suite-config/does_etl_custom/etl/sp.py code served (permissive licence) · get_code("f40b9122a684ff95") |
| mechid2label | Not yet run | pps-lab/cohere/hyperparam-explorer/hyperparam_explorer/hyperparameter/budget_info.py code served (permissive licence) · get_code("39a03b60fdd3438b") |
| preprocess | Not yet run | pps-lab/cohere/doe-suite-config/does_etl_custom/etl/sp.py code served (permissive licence) · get_code("758a7f7410b5f179") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
The need for a privacy management layer in today's systems started to manifest with the emergence of new systems for privacy-preserving analytics and privacy compliance. As a result, many independent efforts have emerged that try to provide system support for privacy. Recently, the scope of privacy solutions used in systems has expanded to encompass more complex techniques such as Differential Privacy (DP). The use of these solutions in large-scale systems imposes new challenges and requirements. Careful planning and coordination are necessary to ensure that privacy guarantees are maintained across a wide range of heterogeneous applications and data systems. This requires new solutions for managing and allocating scarce and non-replenishable privacy resources. In this paper, we introduce Cohere, a new system that simplifies the use of DP in large-scale systems. Cohere implements a unified interface that allows heterogeneous applications to operate on a unified view of users' data. In this work, we further address two pressing system challenges that arise in the context of real-world deployments: ensuring the continuity of privacy-based applications (i.e., preventing privacy budget depletion) and effectively allocating scarce shared privacy resources (i.e., budget) under complex preferences. Our experiments show that Cohere achieves a 6.4--28x improvement in utility compared to the state-of-the-art across a range of complex workloads.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2301.08517")
get_code_for_paper("2301.08517")
have("2301.08517")
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