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add security and privacy chapter initial draft #66

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ae5fcb5
add security and privacy chapter draft
eliasab16 Nov 21, 2023
8a7a29c
Removed TOC
profvjreddi Nov 22, 2023
753f893
Cleaning up MD export issues
profvjreddi Nov 22, 2023
e01c01b
More MD export fixes
profvjreddi Nov 22, 2023
f79f248
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profvjreddi Nov 22, 2023
45abfbb
figure fixes
profvjreddi Nov 22, 2023
781ec32
More MD fixes
profvjreddi Nov 22, 2023
6ed6302
Fixed MD tables
profvjreddi Nov 22, 2023
8cb0ad1
Added learning objectives
profvjreddi Nov 22, 2023
37e7636
Added cover image
profvjreddi Nov 22, 2023
506ccd1
fix MD formatting
profvjreddi Nov 22, 2023
156d651
MD fix
profvjreddi Nov 22, 2023
09f8241
changed typos in introdution.
ELSuitorHarvard Nov 22, 2023
7cbd858
Removing line break
profvjreddi Nov 22, 2023
a0cdf0e
added references through Security Threats to ML Hardware as marked in…
ELSuitorHarvard Nov 22, 2023
ad00309
Merge branch 'security_privacy_chapter' of https://github.com/eliasab…
ELSuitorHarvard Nov 22, 2023
dbdbc49
added remaining references as marked in the doc
ELSuitorHarvard Nov 23, 2023
332bb2d
Merge branch 'main' into security_privacy_chapter
ELSuitorHarvard Nov 23, 2023
32732bc
Updated the MD with the relevant bibtex references
profvjreddi Nov 23, 2023
60fedee
Remove google doc lead note
profvjreddi Nov 24, 2023
897ddb4
Video + ref for Mirai botnet
profvjreddi Nov 24, 2023
89aab14
Removed ML re-definition
profvjreddi Nov 24, 2023
a266b64
Bullet list formatting
profvjreddi Nov 24, 2023
3656ce3
Removed line break >
profvjreddi Nov 24, 2023
134284d
Bold
profvjreddi Nov 24, 2023
b55d0bd
Bullet list conversion
profvjreddi Nov 24, 2023
8c0e321
Removing 80 char line break
profvjreddi Nov 24, 2023
996b7b1
Fixed broken learning objectives
profvjreddi Nov 24, 2023
b820e14
Linked to an incorrect QMD filename, fixed it
profvjreddi Nov 26, 2023
5078fb5
Update references.bib
profvjreddi Nov 26, 2023
ce2e009
Fix broken (removed duplicate) references
profvjreddi Nov 27, 2023
60d621f
Merge branch 'main' into pr/66
profvjreddi Nov 27, 2023
4f8acd2
Merge branch 'main' into pr/66
profvjreddi Nov 27, 2023
06ef191
Fixing broken reference
profvjreddi Nov 27, 2023
5817d08
Fixing linebreak merge erros, where words got fused
profvjreddi Nov 29, 2023
602e64f
Fixed all missing references but one
profvjreddi Nov 29, 2023
4202bba
Fixed missing reference on dnn MCU attack
profvjreddi Nov 29, 2023
3c6ea81
Minor fix, thanks to @arbass22
profvjreddi Nov 29, 2023
5e195a7
Formatting update
profvjreddi Nov 29, 2023
4a71604
fixing table
profvjreddi Nov 29, 2023
acf6f09
first pass through comments, mostly adding captions
Nov 29, 2023
3847080
Minor updates to privacy_security.qmd
mpstewart1 Nov 30, 2023
98d62b5
More minor updates to privacy_security.qmd
mpstewart1 Nov 30, 2023
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2 changes: 1 addition & 1 deletion ops.qmd
Original file line number Diff line number Diff line change
Expand Up @@ -234,7 +234,7 @@ Tight coupling between ML model components makes isolating changes difficult. Mo

### Correction Cascades

![Figure 14.3: The flowchart depicts the concept of correction cascades in the ML workflow, from problem statement to model deployment. The arcs represent the potential iterative corrections needed at each stage of the workflow, with different colors corresponding to distinct issues such as interacting with physical world brittleness, inadequate application-domain expertise, conflicting reward systems, and poor cross-organizational documentation. The red arrows indicate the impact of cascades, which can lead to significant revisions in the model development process, while the dotted red line represents the drastic measure of abandoning the process to restart. This visual emphasizes the complex, interconnected nature of ML system development and the importance of addressing these issues early in the development cycle to mitigate their amplifying effects downstream. [@data_cascades]](images/ai_ops/data_cascades.png)
![Figure 14.3: The flowchart depicts the concept of correction cascades in the ML workflow, from problem statement to model deployment. The arcs represent the potential iterative corrections needed at each stage of the workflow, with different colors corresponding to distinct issues such as interacting with physical world brittleness, inadequate application-domain expertise, conflicting reward systems, and poor cross-organizational documentation. The red arrows indicate the impact of cascades, which can lead to significant revisions in the model development process, while the dotted red line represents the drastic measure of abandoning the process to restart. This visual emphasizes the complex, interconnected nature of ML system development and the importance of addressing these issues early in the development cycle to mitigate their amplifying effects downstream. [@sculley2015hidden](images/ai_ops/data_cascades.png)

Building models sequentially creates risky dependencies where later models rely on earlier ones. For example, taking an existing model and fine-tuning it for a new use case seems efficient. However, this bakes in assumptions from the original model that may eventually need correction.

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1,075 changes: 1,029 additions & 46 deletions privacy_security.qmd

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5,404 changes: 3,031 additions & 2,373 deletions references.bib

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