Online booking is now open for the annual Lovelace lecture, which I’m giving in London on March 15th. I’ll be using the event to try to develop a cross-cutting theme that arises from my research but is of wider interest. What do we mean by scale?

Back when computing was expensive, computer science started out worrying about how computations scaled – such as which sorting algorithms ran as n², n log n or even n. Once software development became the bottleneck, we started off by trying the same approach (measuring code entropy or control graph complexity), but moved on to distinguishing what types of complexity could be dealt with by suitable tools. Mass-market computing and communications brought network effects, and scalability started to depend on context (this is where security economics came in). Now we move to “Big Data” the dependency on people becomes more explicit. Few people have stopped to think of human factors in scaling terms. Do we make information about many people available to many, or to few? What about the complexity of the things that can be done with personal data? What about costs now versus in the future, and the elasticity of demand associated with such costs? Do you just count the data subjects, do you count the attackers too, or do you add the cops as well?

I’ve been quoted as saying “You can have security, functionality, scale – choose any two” or words to that effect. I’ll discuss this and try to sketch the likely boundaries, as well as future research directions. The discussion will cross over from science and engineering to economics and politics; recent proposed legislation in the UK, and court cases in the USA, would impose compliance burdens on people trying to scale systems up from one country to many.

The students we’re training to be the next generation of developers and entrepreneurs will need a broader understanding of what’s involved in scaling systems up, and in this talk I’ll try to explore what that means. Maybe I’m taking a risk with this talk, as I’m trying to assemble into a row a lot of facts that are usually found in different columns. But I do hope it will not be boring.