43 lines
3.5 KiB
Markdown
43 lines
3.5 KiB
Markdown
# Calendar (Tentative)
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For differential privacy, we will use the textbook *Algorithmic Foundations of
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Data Privacy* (AFDP) by Cynthia Dwork and Aaron Roth, available
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[here](https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf).
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Date | Topic | Presenter
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:----:|-------|:---------:
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| <center> <h4> **Differential Privacy** </h4> </center> |
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9/5 | [Course welcome, introducing differential privacy](../resources/slides/lecture01.html) <br> **Paper:** Keshav. [*How to Read a Paper*](https://web.stanford.edu/class/ee384m/Handouts/HowtoReadPaper.pdf). | JH
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9/10 | Basic private mechanisms <br> **Reading:** AFDP 3.2, 3.3 | JH
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9/12 | What does differential privacy actually mean? <br> **Reading:** McSherry. [*Lunchtime for Differential Privacy*](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-16.md) (see also these [two](https://github.com/frankmcsherry/blog/blob/master/posts/2016-06-14.md) [posts](https://github.com/frankmcsherry/blog/blob/master/posts/2016-08-29.md)) | JH
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9/17 | Composition and closure properties <br> **Reading:** AFDS 3.5 <br> <center> <h5> **Due: Project topics and groups** </h5> </center> | JH
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9/19 | Exponential mechanism <br> **Paper:** McSherry and Talwar. [*Mechanism Design via Differential Privacy*](http://kunaltalwar.org/papers/expmech.pdf). | JH
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**9/21 (FRI)** | Identity-Based Encryption from the Diffie-Hellman Assumption <br> <center> **SPECIAL TIME AND PLACE: 4 PM, CS 1240** </center> | Sanjam Garg
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9/24 | Privacy for data streams <br> **Paper:** Chan, Shi, and Song. [*Private and Continual Release of Statistics*](https://eprint.iacr.org/2010/076.pdf). |
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9/26 | Report-noisy-max and the Sparse Vector Technique **Reading:** AFDS 3.3, 3.5 | JH
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10/1 | Answering lots of queries: Private multiplicative weights <br> **Paper:** Hardt, Ligett, and McSherry. [*A Simple and Practical Algorithm for Differentially Private Data Release*](https://papers.nips.cc/paper/4548-a-simple-and-practical-algorithm-for-differentially-private-data-release.pdf). |
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10/3 | Local differential privacy (theory) AFDS 12.1 | JH
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10/8 | Local differential privacy (practice) <br> **Paper:** Erlingsson, Pihur, and Korolova. [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf). |
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10/10 | More differential privacy <br> **Paper:** |
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10/15 | <center> **NO CLASS: INSTRUCTOR AWAY** </center> |
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10/17 | <center> **NO CLASS: INSTRUCTOR AWAY** <br> <center> <h5> **Due: Milestone 1** </h5> </center> |
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| <center> <h4> **Cryptographic Techniques** </h4> </center> |
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10/22 | Crypto: overview and basics | JH
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10/24 | Secure multiparty computation <br> **Paper:** |
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10/29 | Homomorphic encryption <br> **Paper:** |
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10/31 | Verifiable computing <br> **Paper:** |
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11/5 | More applied crypto <br> **Paper:** |
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| <center> <h4> **Language-Based Security** </h4> </center> |
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11/7 | LangSec: overview and basics | JH
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11/12 | Secure Information Flow <br> **Paper:** |
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11/14 | Languages for privacy <br> **Paper:** <br> <center> <h5> **Due: Milestone 2** </h5> </center> |
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11/19 | Symbolic cryptography <br> **Paper:** |
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11/21 | More LangSec <br> **Paper:** |
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> |
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11/26 | AML: overview and basics <br> <center> **GUEST LECTURE** </center> | Somesh Jha
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11/28 | AML: overview and basics <br> <center> **GUEST LECTURE** </center> | Somesh Jha
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12/3 | Adversarial examples <br> **Paper:** |
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12/5 | Training-time attacks <br> **Paper:** |
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12/10 | Model-theft attacks <br> **Paper:** |
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12/12 | More AML <br> **Paper:** |
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