Graphistania 2.0 - Episode 4 - This Month in Neo4j

Yey! My friend StefanW and I got round to recording another Graphistania episode, episode 4 already - time flies when you are having fun! This month, again, we have so much great content popping up in the This Week in Neo4j (Twin4j) newsletter, that we could probably fill a few hours talking about it. So in the podcast, we will only talk about a handful - covering things like

• the great momentum that we have in the Neo4jcommunity! Take a look at initiatives like Global Graph Celebration Day, and listen to Neo4j's mad scientist Michael Hunger on graphs, databases and relationships on a different podcast.
• the start of the Neo4j Graphtour, for the 3rd year already, in Amsterdam. This year this actually coincided with the launch of Neo4j 4.0 - one of the best releases of Neo4j that I have personally ever witnessed. I have actually been writing a bit about this myself - read about how I how I child-proofed my beergraph with fine-grained security and how I did something similar by adding security to a fraud investigation graph. There's other articles as well, like When and how to implement Sharding in Neo4j 4.0.
• Various personal graphs projects, like Mark's Australian Open graph and the QuickGraph #3 on Itsu Allergens. I love how Mark summarized it: most of analysis could be done in relational, but "Things got more interesting in the last section where we did set analysis. I found having the data in a graph structure made was helpful for answering these questions, especially when we were looking for the non existence of a relationship."
• a number of Health related articles, like that visualisation of the data from the Personal Genome Project, or the Google project wiuth the “Largest Ever” Map of Brain Connectivity
• And then of course there were various "Other" posts that we really liked, like how to be Working With Spatial Data In Neo4j GraphQL In The Cloud, and the post about Aaia - AWS Identity And Access Management Visualizer And Anomaly Finder. Last but not least, you should also take a look at the Graphaware Hume platform - with an excellent powerful demo recording over here.

Experimenting with Conflicting access privileges in Neo4j 4.0

In the past couple of weeks, I have been playing around with the shiny new security features of Neo4j 4.0. They are truly interesting - both for childproofing beergraphs and for ensuring that your sensitive fraud databases are properly secured. Take a look at the previous post, and I think you will understand why.

In this post, I wanted to talk about something that I have seen so many times in my previous lives in the security industry, and that also became evident in my 4.0 research. It's got to do with conflicting security privileges. In a nutshell, this is to do with the case where

• a specific user / role would receive a particular set of privileges from one policy
• the same user / role would receive a different, and contradictory privilege from another policy. 

In that case, we need clear rules to understand what would happen. In the case of Neo4j 4.0, this is reasonably well explained as part of the documentation - see the documentation site on this topic - but in this post I will try to give you a realistic, but simple example.

Creating Conflict

We'll start working on this with the same database as the previous post, the fraud dataset. If you don't have it yet, just download it from this link. Once we have the database up and running as a separate user database, we can switch to the system database and create a separate user for these tests.

//create a separate user for engineering the conflicting privileges
CREATE USER conflicted_user SET PASSWORD "changeme" CHANGE NOT REQUIRED;
CREATE ROLE conflicted_role AS COPY OF reader;

Securing a sample fraud graph with Neo4j 4.0

This week, we at Neo4j formally released our brightest and shiniest new version of the Neo4j Graphg Database to the world. It's been an amazing journey to this point, and others have reported on this magnificent piece of engineering in more depth. Take a look at Jim's blogpost, or if you are in a hurry, checkout the graphcast below:

Last week, I started playing around with it myself - by digging up my good old faithful beergraph, and illustrating some of the new features in childproofing exercise for beers. Take a look at that post as well for some giggles. Now in this post, I wanted to essentially do the same thing as I did on the beergraph, but using a Fraud dataset. 

Let's see how that would work.