43 lines
5.5 KiB
Markdown
43 lines
5.5 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). | Justin
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9/10 | Basic private mechanisms <br> **Reading:** AFDP 3.2, 3.3 | Justin
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9/12 | Composition and closure properties <br> **Reading:** AFDP 3.5 | Justin
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9/17 | 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)) | Justin
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9/19 | Exponential mechanism <br> **Paper:** McSherry and Talwar. [*Mechanism Design via Differential Privacy*](http://kunaltalwar.org/papers/expmech.pdf). <br> <center> <h5> **Due: Project topics and groups** </h5> </center> | Justin
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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 | Advanced mechanisms <br> Report-noisy-max, Sparse Vector Technique, and Private Multiplicative Weights <br> **Reading:** AFDP 3.3, 3.5, 4.2 | Justin
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9/26 | Privacy for data streams <br> **Paper:** Chan, Shi, and Song. [*Private and Continual Release of Statistics*](https://eprint.iacr.org/2010/076.pdf). | Yinglun
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10/1 | Local differential privacy <br> **Paper:** Erlingsson, Pihur, and Korolova. [*RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response*](https://arxiv.org/pdf/1407.6981.pdf). | Justin
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| <center> <h4> **Adversarial Machine Learning** </h4> </center> |
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10/3 | [AML: overview and basics](../resources/slides/somesh-aml.pdf) <br> <center> **GUEST LECTURE** </center> | Somesh Jha
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10/8 | History of Adversarial ML <br> **Paper:** Biggio and Roli. [*Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning*](https://arxiv.org/pdf/1712.03141). | Meghana
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10/10 | Adversarial examples <br> **Paper:** Szegedy, Zaremba, Sutskever, et al. [*Intriguing Properties of Neural Networks*](https://arxiv.org/pdf/1312.6199.pdf). | Shimaa
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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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10/22 | Adversarial examples <br> **Paper:** Goodfellow, Schlens, and Szegedy. [*Explaining and Harnessing Adversarial Examples*](https://arxiv.org/abs/1412.6572). | Kyrie
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10/24 | Real-world attacks <br> **Paper:** Eykholt, Evtimov, Fernandes, et al. [*Robust Physical-World Attacks on Deep Learning Models*](https://arxiv.org/pdf/1707.08945.pdf). | Hiba
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10/29 | Detection methods <br> **Paper:** Carlini and Wagner. [*Towards Evaluating the Robustness of Neural Networks*](https://arxiv.org/pdf/1608.04644.pdf). | Yiqin
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10/31 | Detection methods <br> **Paper:** Carlini and Wagner. [*Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods*](https://arxiv.org/pdf/1705.07263.pdf). | Junxiong
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11/5 | Defensive measures <br> **Paper:** Steinhardt, Koh, and Liang. [*Certified Defenses for Data Poisoning Attacks*](https://arxiv.org/pdf/1706.03691.pdf). | Yaman
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11/7 | Defensive measures <br> **Paper:** Madry, Makelov, Schmidt, Schmidt, Tsipras, and Valdu. [*Towards Deep Learning Models Resistant to Adversarial Attacks*](https://arxiv.org/pdf/1706.06083.pdf). | Maddy
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| <center> <h4> **Cryptographic Techniques** </h4> </center> |
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11/12 | Applied crypto: overview and basics | Justin
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11/19 | Verifiable differential privacy <br> **Paper:** Narayan, Feldman, Papadimitriou, and Haeberlen. [*Verifiable Differential Privacy*](https://www.cis.upenn.edu/~ahae/papers/verdp-eurosys2015.pdf). <br> <center> <h5> **Due: Milestone 2** </h5> </center> | Fayi
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11/19 | Homomorphic encryption <br> **Paper:** Ducas and Micciancio. [*FHEW: Bootstrapping Homomorphic Encryption in Less than a Second*](https://eprint.iacr.org/2014/816.pdf). | Yue
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| <center> <h4> **Language-Based Security** </h4> </center> |
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11/21 | Language-based security: overview and basics | Justin
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11/26 | Languages for privacy <br> **Paper:** Reed and Pierce. [*Distance Makes the Types Grow Stronger: A Calculus for Differential Privacy*](https://www.cis.upenn.edu/~bcpierce/papers/dp.pdf). | Sam
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11/28 | <center> **TBA** </center> |
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12/3 | Languages for authenticated datastructures <br> **Paper:** Miller, Hicks, Katz, and Shi. [*Authenticated Data Structures, Generically*](https://www.cs.umd.edu/~mwh/papers/gpads.pdf). | Zichuan
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12/5 | Languages for oblivous computing <br> **Paper:** Zahur and Evans. [*Obliv-C: A Language for Extensible Data-Oblivious Computation*](https://eprint.iacr.org/2015/1153.pdf). | Zhiyi
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12/10 | Languages for information flow <br> **Paper:** Griffin, Levy, Stefan, et al. [*Hails: Protecting Data Privacy in Untrusted Web Applications*](https://www.usenix.org/system/files/conference/osdi12/osdi12-final-35.pdf). | Arjun
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12/12 | Languages for preventing timing channels <br> **Paper:** Zhang, Askarov, and Myers. [*Language-Based Control and Mitigation of Timing Channels*](https://www.cs.cornell.edu/andru/papers/pltiming-pldi12.pdf). | Yan
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