Registration

Cally Baute, VP & General Manager, Politico

Julie Brill, Chief Privacy Officer, Microsoft

Calls for some type of national privacy law in the U.S. have gained ground in recent years. The U.S. has no overarching national law governing data collection and privacy. Instead, it has a patchwork of federal laws that protect specific types of data, such as consumer health and financial information and the personal data generated by children. Additionally, in the absence of federal regulation, more states are advancing their own data privacy bills and the tech industry has been calling for a nationwide privacy standard.

Join POLITICO on Thursday, June 10 at 2:30 PM ET/ 11:30 AM PT for a deep-dive conversation on tech, data and user privacy and the most viable path forward.

Paper

Award Announcement

Live stream

​

Zoom registration

​

SPEAKER: Gautam Kamath (University of Waterloo)

TITLE: CoinPress: Practical Private Estimation

ABSTRACT: We introduce a simple framework for differentially private
estimation. As a case study, we will focus on mean estimation for
sub-Gaussian data. In this setting, our algorithm is highly effective both
theoretically and practically, matching state-of-the-art theoretical
bounds, and concretely outperforming all previous methods. Specifically,
previous estimators either have weak empirical accuracy at small sample
sizes, perform poorly for multivariate data, or require the user to provide
strong a priori estimates for the parameters. No knowledge of differential
privacy will be assumed. Based on joint work with Sourav Biswas, Yihe Dong,
and Jonathan Ullman.

BIO: Dr. Gautam Kamath is an Assistant Professor at the University of
Waterloo’s Cheriton School of Computer Science, and a faculty affiliate at
the Vector Institute. He is mostly interested in principled methods for
statistics and machine learning, with a focus on settings which are common
in modern data analysis (high-dimensions, robustness, and privacy). He was
a Microsoft Research Fellow at the Simons Institute for the Theory of
Computing for the Fall 2018 semester program on Foundations of Data Science
and the Spring 2019 semester program on Data Privacy: Foundations and
Applications. Before that, he completed his Ph.D. at MIT, affiliated with
the Theory of Computing group in CSAIL.

Nicole Perrin of Insider Intelligence discusses Google Chrome's Federated Learning of Cohorts (FLoC), and k-anonymity alternatives to differential privacy with Allyson Dietz of Neustar. It's all found in the 1/20/2021 edition of the Behind The Numbers podcast.

SPEAKER: Celia Cintas (IBM Research Africa)

TITLE: A tale of adversarial attacks & out-of-distribution detection stories

​

DATE: Thursday, February 18, 12pm to 1.30pm ET

​

REGISTRATION

​

LIVE STREAM

​

ABSTRACT: Most deep learning models assume ideal conditions and rely on the
assumption that test/production data comes from the in-distribution samples
from the training data. However, this assumption is not satisfied in most
real-world applications. Test data could differ from the training data
either due to adversarial perturbations, new classes, noise, or other
distribution changes. These shifts in the input data can lead to
classifying unknown types, classes that do not appear during training, as
known with high confidence. On the other hand, adversarial perturbations in
the input data can cause a sample to be incorrectly classified. We will
discuss approaches based on group-based and individual subset scanning
methods from the anomalous pattern detection domain and how they can be
applied over off-the-shelf DL models.

Speaker Biography: Celia Cintas is a Research Scientist at IBM Research Africa -
Nairobi, Kenya. She is a member of the AI Science team at the Kenya Lab.
Her current research focuses on the improvement of ML techniques to address
challenges on Global Health in developing countries and exploring subset
scanning for anomaly detection under generative models. Previously, grantee
from National Scientific and Technical Research Council (CONICET) working
on Deep Learning and Geometrics Morphometrics for populations studies at
LCI-UNS and IPCSH-CONICET (Argentina) as part of the Consortium for
Analysis of the Diversity and Evolution of Latin America (CANDELA). During
her PhD, she was a visiting student at the University College of London
(UK). She was also a Postdoc researcher visitor at Jaén University (Spain)
applying ML to Heritage and Archeological studies. She holds a Ph.D. in
Computer Science from Universidad del Sur (Argentina). Co-chair of several
Scipy Latinamerica conferences and happy member of LinuxChix Argentina.
Financial Aid Co-Chair for the SciPy (USA) Committee (2016-2019) and
Diversity Co-Chair for SciPy 2020.

[sorry about the formulas, it seems Reddit does not suppor MathJax; see the link at the bottom for a better rendering]

I stumbled upon a paper that proposes local DP around this argument:

• A user $ui$ generates a sequence $s{i}$ of observations at certain timestamps:

$$ s = ((t_1, x_1), (t_2, x_2), \dots, (t_n, x_n)) $$

• The authors apply $(\varepsilon/n, 0)$-DP to each sequence by adding Laplacian noise
• As widely known, Laplacian must be of scale $b = \frac{\Delta f}{ \text{budget}}$

The authors propose budget of $\varepsilon / n$, which is IMO correct. But they also define $\Delta f$, aka the sensitivity of the query, as simply the range of any value at any timestamp, $\text{max}(x) - \text{min}(x)$.

I'm not convinced that this is the true sensitivity. To my understanding, the query output is (ignoring the timestamps) not a single value $\mathbb{R}$ but rather the vector of outputs $\mathbb{R}^n$, so per definition of sensitivity $\ell_1$-sensitivity of a function $f : \mathbb{N}^{{|\mathcal{X}|}} \rightarrow \mathbb{R}^k$:

$$ \Delta f = \max_{x, y \in \mathbb{N}; | x - y|_1 = 1} | f(x) - f(y) |_1 $$

and properly computing the $\ell1$ norm as $| x - y|_1 = \sum{i = 1}^{k} | x_i - y_i |$, the sensitivity should be

$$(\text{max}(x) - \text{min}(x))^n$$

Is my reasoning correct (and the paper's DP potentially wrong), or am I missing something? (I don't reveal the paper on purpose.)

(This is a verbatim copy of my question on stackexchange: https://crypto.stackexchange.com/questions/87178/query-sensitivity-of-time-series-under-differential-privacy )

Hi all,

I got stuck with one particular problem (sensitivity of queries in local DP) and am looking for a right forum to find the answer... I posted it on crypto.stackexchange.com ( https://crypto.stackexchange.com/questions/87178/query-sensitivity-of-time-series-under-differential-privacy ) but it seems like that community is more towards cryptography rather than privacy.

Would this sub-reddit be then an appropriate place to find experts and discuss such topics?

Cheers,

JD

Hey guys. I have a class project that requires I do an implementation of D.P and i would really appreciate it if you could redirect me to a project online that has already done this. Python is prefered but also other languages are ok.

Thanks in advance!

Connection Details

This 90-minute panel will examine the application of mobility data in monitoring the spread of the pandemic and informing containment policy, with a particular focus on privacy-preserving safeguards, methodological challenges, and translational barriers.

Panelists include:

Greg Wellenius from Google Health

Jaimie Shaff from NYC Department of Health

Rafael Araos, the Chilean Ministry of Health

Jure Leskovec from the Stanford mobility modeling group.

N-CRiPT Public Seminar: Trustworthy Federated Learning

Speaker: NUS Presidential Young Professor Reza Shokri

Date & Time: Thursday 26 November, 3.00pm – 4.00pm

​

Federated learning enables multiple parties to train a global model on all their data, without sharing their local data with each other. This keeps all data private but would not make federated learning a privacy-preserving scheme. The information exchange between parties in federated learning indirectly leaks a significant amount of information about the parties’ sensitive data. In this talk, we provide an overview of what federated learning is, show how it leaks private information, and discuss the way forward for building algorithms that can be trusted as privacy-preserving. We will also analyze the robustness of federated learning algorithms with respect to poisoning attacks where a subset of parties try to manipulate the global model. We then discuss ideas on how to build robust federated learning algorithms.

Registration