CS 848: Privacy Enhancing Data Systems 

Winter 2024
Instructor: Sujaya Maiyya
Seminar time and location: 10-11:20AM TTh at DC 2568

Course Description

More than 94% of current enterprises – including 83% of healthcare organizations – rely on cloud services, especially for their infrastructure and data storage needs. Organizations outsource their storage to third party cloud providers because of the high cost associated with owning and maintaining an on-premise storage or compute fleet. However, outsourcing an application’s data in plaintext can reveal sensitive information to a potentially nontrustworthy cloud provider. While encrypting the data forms the first obvious solution to ensure data privacy, a growing body of attacks exploit side-channel information such as access frequency or duration on encrypted data to uncover plaintext data. Such attacks are called inference or access pattern attacks. For example, the duration and frequency with which an oncologist accesses (encrypted) data can reveal the type of a patient’s cancer (e.g., based on the frequency and intervals of chemotherapy treatments).

In this course, we will study the design and implementation of private data systems that protect applications against access pattern attacks. We begin the course by reviewing the basic cryptographic encryption schemes, followed by understanding an encrypted database and the potential leakages in it as a motivation to learn about systems with stronger privacy guarantees. This course will cover data systems that use four types of cryptographic primitives: oblivious RAM, trusted hardware enclaves, secure multi party computation, and private information retrieval. Note that since this course is offered under data systems category, the discussions will focus primarily of the database design concepts.

Assessment

The final grade for the course will be based on the following components:

Reviews (20%): Each week will have at least two papers that students are expected to read and write the reviews of. Each review should be no more than 500 words and contain the following sections, following the typical format of reviews in database conferences:

Submit the reviews by 1pm on the day (Monday or Wednesday) before the lecture. Please submit your reviews on this website: https://uwaterloo-cs848w24.hotcrp.com/. All reviews will be made public (anonymously) by 2pm.

Paper Presentation (15%): Unless otherwise stated in the schedule, students will present one paper in each seminar session. Each presentation should be for 25-30 minutes, which will be followed by 30 minutes of discussion. For each presentation, one student will act as a primary and one as a secondary. Presentations are prepared by the primary student. Presentations should answer the following questions and end with a question that can start a discussion in class: The secondary student will read the reviews of all the other students made public to aid the paper discussion. Note that you do not need to write a review of the paper you are the primary for a paper. Each student is expected to be the primary and present 1-2 papers, and act as a secondary for 1-2 papers. If the presentation load becomes more depending on the enrollment, the weight will be changed to 20%.

Class participation (15%): After the presentation, we will spend 30 minutes discussing the paper. Since this is a seminar course, this discussion is very important. The expectation is that everyone in class will participate.

Project (50%): Students are expected to work (individually or in groups of two) on original research projects related to data privacy. There are three deliverables for the project:

Logistics

Schedule

Week Dates Topic Speaker Readings
1 Jan 9th Intro Sujaya Maiyya Slides
Jan 11th Background: crypto and DB basics. Sujaya Maiyya Required: Textbook Chapter 0 and Chapter 2. Recommend reading other chapters as well.
Slides
2 Jan 16th Intro to ORAM Sujaya Maiyya Reading required but review not required: (1) PathORAM up to Section 4. (2) RingORAM.
Slides
Jan 18th CrypDB Student 1 Required: CryptDB: protecting confidentiality with encrypted query processing
3 Jan 23rd Attack on CryptDB Student 2 Required: Inference Attacks on Property-Preserving Encrypted Databases
Jan 25th Concurrent ORAM (1) - Proxy based Student 3 Required: TaoStore: Overcoming Asynchronicity in Oblivious Data Storage
4 Jan 30th Concurrent ORAM (2) - Proxy-less Student 4 Required: ConcurORAM: High-Throughput Stateless Parallel Multi-Client ORAM
Feb 1st Transactions in ORAM Student 5 Required: Obladi: Oblivious Serializable Transactions in the Cloud
5 Feb 6th Replicated ORAM Student 6 Required: QuORAM: A Quorum-Replicated Fault Tolerant ORAM Datastore
Feb 8th Alternate techniques to ORAM (1) Student 7 Required: Pancake: Frequency Smoothing for Encrypted Data Stores
6 Feb 13th Alternate techniques to ORAM (2) Student 8 Required: Waffle: An Online Oblivious Datastore for Protecting Data Access Patterns
Feb 15th Intro to MPC (both circuit and secret sharing based) and TEEs Sujaya Maiyya Slides
7 Feb 20th No class: Reading week
Feb 22th No class: Reading week
8 Feb 27th Circuit based federated db Student 9 Required: SMCQL: Secure Querying for Federated Databases
Feb 29th Circuit based encrypted DB Student 10 Required: Arx: An Encrypted Database using Semantically Secure Encryption
9 Mar 5th Secret sharing based DB Student 11 Required: Information-Theoretically Secure and Highly Efficient Search and Row Retrieval
Mar 7th TEE-based OLAP DB Student 12 Required: Opaque: An Oblivious and Encrypted Distributed Analytics Platform
10 Mar 12th TEE-based oblivious DB Student 13 Required: ObliDB: Oblivious Query Processing for Secure Databases
Mar 14th TEE-based oblivious scalable DB Student 14 Required: Snoopy: Surpassing the Scalability Bottleneck of Oblivious Storage
11 Mar 19th Intro to private information retrieval Sujaya Maiyya Slides
Mar 21th PIR based kv store Student 15 Required: Pantheon: Private Retrieval from Public Key-Value Store
12 Mar 26th FSS-based PIR data system Student 16 Required: Splinter: Practical Private Queries on Public Data
Mar 28th Demo or paper (based on class size) Demo or Student 17
13 Apr 2nd Project demos
Apr 4th Project demos
Note that the schedule is tentative and subject to change. We may consider online mode for the class for special situations.

Course policy and other information

Plagiarism

Plagiarism is a very serious academic offence and is penalized accordingly. When you plagiarize you damage the learning experience for yourself and others. To avoid plagiarism accusations, do not copy other people's work, and cite all references that you use. If you work with others, only discuss general aspects of the course material, not specific solutions. Write up the solutions yourself, not in groups.

Mental Health Resources

Mental Health: If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support.

On-campus Resources

Off-campus Resources

Diversity: It is our intent that students from all diverse backgrounds and perspectives be well served by this course, and that students’ learning needs be addressed both in and out of class. We recognize the immense value of the diversity in identities, perspectives, and contributions that students bring, and the benefit it has on our educational environment. Your suggestions are encouraged and appreciated. Please let us know ways to improve the effectiveness of the course for you personally or for other students or student groups. In particular:

University Policies (University required text)

Academic Integrity:
In order to maintain a culture of academic integrity, members of the University of Waterloo community are expected to promote honesty, trust, fairness, respect and responsibility. All members of the UW community are expected to hold to the highest standard of academic integrity in their studies, teaching, and research. The Office of Academic Integrity's website ( http://www.uwaterloo.ca/academicintegrity) contains detailed information on UW policy for students and faculty. This site explains why academic integrity is important and how students can avoid academic misconduct. It also identifies resources available on campus for students and faculty to help achieve academic integrity in - and out - of the classroom.

Grievance:
A student who believes that a decision affecting some aspect of his/her university life has been unfair or unreasonable may have grounds for initiating a grievance. Read Policy 70 - Student Petitions and Grievances, Section 4, http://www.adm.uwaterloo.ca/infosec/Policies/policy70.htm .

Discipline:
A student is expected to know what constitutes academic integrity, to avoid committing academic offenses, and to take responsibility for his/her actions. A student who is unsure whether an action constitutes an offense, or who needs help in learning how to avoid offenses (e.g., plagiarism, cheating) or about "rules" for group work/collaboration should seek guidance from the course professor, academic advisor, or the Undergraduate Associate Dean. When misconduct has been found to have occurred, disciplinary penalties will be imposed under Policy 71 - Student Discipline. For information on categories of offenses and types of penalties, students should refer to Policy 71 - Student Discipline, http://www.adm.uwaterloo.ca/infosec/Policies/policy71.htm.

Avoiding Academic Offenses:
Most students are unaware of the line between acceptable and unacceptable academic behaviour, especially when discussing assignments with classmates and using the work of other students. For information on commonly misunderstood academic offenses and how to avoid them, students should refer to the Faculty of Mathematics Cheating and Student Academic Discipline Policy, https://uwaterloo.ca/math/current-undergraduates/regulations-and-procedures/cheating-and-student-academic-discipline-guidelines.

MOSS (Measure of Software Similarities) is used in this course as a means of comparing students' assignments to ensure academic integrity. We will report suspicious activity, and penalties for plagiarism/cheating are severe. Please read the available information about academic integrity very carefully.

Discipline cases involving any automated marking system such as Marmoset include, but are not limited to, printing or returning values in order to match expected test results rather than making an actual reasonable attempt to solve the problem as required in the assignment question specification.

Appeals:
A student may appeal the finding and/or penalty in a decision made under Policy 70 - Student Petitions and Grievances (other than regarding a petition) or Policy 71 - Student Discipline if a ground for an appeal can be established. Read Policy 72 - Student Appeals, https://uwaterloo.ca/secretariat/policies-procedures-guidelines/policy-72 .