InfoShape: Task-Based Neural Data Shaping via Mutual Information Article Swipe
Homa Esfahanizadeh
,
William Ka Kei Wu
,
Manya Ghobadi
,
Regina Barzilay
,
Muriel Médard
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.15034
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2210.15034
The use of mutual information as a tool in private data sharing has remained an open challenge due to the difficulty of its estimation in practice. In this paper, we propose InfoShape, a task-based encoder that aims to remove unnecessary sensitive information from training data while maintaining enough relevant information for a particular ML training task. We achieve this goal by utilizing mutual information estimators that are based on neural networks, in order to measure two performance metrics, privacy and utility. Using these together in a Lagrangian optimization, we train a separate neural network as a lossy encoder. We empirically show that InfoShape is capable of shaping the encoded samples to be informative for a specific downstream task while eliminating unnecessary sensitive information. Moreover, we demonstrate that the classification accuracy of downstream models has a meaningful connection with our utility and privacy measures.
Related Topics
Concepts
Mutual information
Computer science
Task (project management)
Encoder
Machine learning
Artificial neural network
Artificial intelligence
Downstream (manufacturing)
Estimator
Measure (data warehouse)
Information sensitivity
Data mining
Engineering
Operating system
Operations management
Mathematics
Computer security
Systems engineering
Statistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2210.15034
- https://arxiv.org/pdf/2210.15034
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4307535076
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4307535076Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2210.15034Digital Object Identifier
- Title
-
InfoShape: Task-Based Neural Data Shaping via Mutual InformationWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-10-26Full publication date if available
- Authors
-
Homa Esfahanizadeh, William Ka Kei Wu, Manya Ghobadi, Regina Barzilay, Muriel MédardList of authors in order
- Landing page
-
https://arxiv.org/abs/2210.15034Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2210.15034Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2210.15034Direct OA link when available
- Concepts
-
Mutual information, Computer science, Task (project management), Encoder, Machine learning, Artificial neural network, Artificial intelligence, Downstream (manufacturing), Estimator, Measure (data warehouse), Information sensitivity, Data mining, Engineering, Operating system, Operations management, Mathematics, Computer security, Systems engineering, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.Lagrangian | 86 |
| abstract_inverted_index.connection | 136 |
| abstract_inverted_index.difficulty | 20 |
| abstract_inverted_index.downstream | 116, 131 |
| abstract_inverted_index.estimation | 23 |
| abstract_inverted_index.estimators | 64 |
| abstract_inverted_index.meaningful | 135 |
| abstract_inverted_index.particular | 52 |
| abstract_inverted_index.task-based | 33 |
| abstract_inverted_index.demonstrate | 125 |
| abstract_inverted_index.eliminating | 119 |
| abstract_inverted_index.empirically | 99 |
| abstract_inverted_index.information | 4, 41, 49, 63 |
| abstract_inverted_index.informative | 112 |
| abstract_inverted_index.maintaining | 46 |
| abstract_inverted_index.performance | 76 |
| abstract_inverted_index.unnecessary | 39, 120 |
| abstract_inverted_index.information. | 122 |
| abstract_inverted_index.optimization, | 87 |
| abstract_inverted_index.classification | 128 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile.value | 0.12852097 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |