The London “festival of A.I. and emerging technology” that takes place each June.

This year, due to Covid-19, the event took place completely online.

(For more about how CogX pulled that off, look here.)

One of the sessions veered towards privacy.

One of the most interesting sessions I tuned into was on privacy-preserving machine learning. This is becoming a hot topic, particularly in healthcare, and especially now due to the interest in applying machine learning to healthcare records that the coronavirus pandemic is helping to accelerate.

The Hook Up puts 10 indoor cameras to the test to figure out which one gives the most features while retaining your privacy.

My top 3 choices for those without the ability (or desire) to block cameras from the internet:

  1. EufyCam Pan & Tilt (Ships June): https://www.eufylife.com/activities/indoorcampreorder 
  2. IoTeX UCAM (Ships July): https://ucam.iotex.io/ 
  3. WyzeCam V2 (Shipping Now): https://amzn.to/2ZVdTWZ

Using data for machine learning and analytics can potentially expose private data. 

How can we leverage data while ensuring that private information remains private?

In this video, learn how differential privacy can be used to preserve privacy and get a demo on how you can use newly released open source system, WhiteNoise, to put DP into your applications.

Learn More:

Databricks  recently hosted this online tech talk on Delta Lake.

The General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) both aim to guarantee strong protection for individuals regarding their personal data and apply to businesses that collect, use, or share consumer data, whether the information was obtained online or offline. This remains one of the top priorities for the companies to be compliant and they are spending a lot of time and resources on being GDPR and CCPA compliant.

Your organization may manage hundreds of terabytes worth of personal information in your cloud. Bringing these datasets into GDPR and CCPA compliance is of paramount importance, but this can be a big challenge, especially for larger datasets stored in data lakes.

Can security surveillance systems and associated analytics work in a station environment without disrupting the rail network?

An interesting competition us underway in the UK that will surely raise privacy and security concerns the world over.

The Innovate UK challenge aims to develop a system capable of automatically detecting potential passenger/rail safety issues, intending to enhance manual control room activities and station security and safety as a whole. The vision is to have video data inform control room operators of potential problems as and even before they occur.

The Innovate UK challenge aims to develop a system capable of automatically detecting potential passenger/rail safety issues, intending to enhance manual control room activities and station security and safety as a whole. The vision is to have video data inform control room operators of potential problems as and even before they occur.

By now, it’s clear that COVID-19 has become a significant threat to public health globally, prompting many governments to undertake draconian measures to contain or curtail the epidemic.

Most governments are relying on travel restrictions, isolation, and social distancing as the preeminent methods of stopping the spread of the virus.

What if we were to be more surgical in our approach using location data collected from our devices?

We start with the subset of people who we know tested positive. Using cellphone tower data, we can figure out where these infected people have been and how long they have stayed in each location. Epidemiologists tell us that transmission is most likely to occur between people who are within one meter of each other for 15 minutes or more. We know that infections can also happen because the virus can survive on surfaces, and the analysis could incorporate this observation too, but for simplicity’s sake, we leave it out of analysis here.

Lex Fridman shared this lecture by Andrew Trask in January 2020, part of the MIT Deep Learning Lecture Series.

OUTLINE:

0:00 – Introduction
0:54 – Privacy preserving AI talk overview
1:28 – Key question: Is it possible to answer questions using data we cannot see?
5:56 – Tool 1: remote execution
8:44 – Tool 2: search and example data
11:35 – Tool 3: differential privacy
28:09 – Tool 4: secure multi-party computation
36:37 – Federated learning
39:55 – AI, privacy, and society
46:23 – Open data for science
50:35 – Single-use accountability
54:29 – End-to-end encrypted services
59:51 – Q&A: privacy of the diagnosis
1:02:49 – Q&A: removing bias from data when data is encrypted
1:03:40 – Q&A: regulation of privacy
1:04:27 – Q&A: OpenMined
1:06:16 – Q&A: encryption and nonlinear functions
1:07:53 – Q&A: path to adoption of privacy-preserving technology
1:11:44 – Q&A: recommendation systems