# Heuristics and Occam’s razor for Counter Terrorism

When overworked analysts use shortcuts to search huge noisy dirty databases, they create trails which can be mined for actual heuristics

Heuristics –

A heuristic technique (/hjᵿˈrɪstk/; Ancient Greek: εὑρίσκω, “find” or “discover”), often called simply a heuristic, is any approach to problem solving, learning, or discovery that employs a practical method not guaranteed to be optimal or perfect, but sufficient for the immediate goals. Where finding an optimal solution is impossible or impractical, heuristic methods can be used to speed up the process of finding a satisfactory solution

Example- A Police chief in Chicago may adopt different heuristics than in New York than in New Orleans for allocating human resources

Solution- Make a database of heuristics as actually in practice for that particular domain

Additional Solution- Search Companies to partner not just in giving data but also training and in some case search algorithms for database analysis and database design reviews of Homeland Security

Occam’s Razor-

Occam’s razor (also written as Ockham’s razor, and lex parsimoniae in Latin, which means law of parsimony) is a problem-solving principle attributed to William of Ockham (c. 1287–1347), who was an English Franciscan friar, scholastic philosopher and theologian. The principle can be interpreted as stating Among competing hypotheses, the one with the fewest assumptions should be selected.

https://en.wikipedia.org/wiki/Occam%27s_razor

https://en.wikipedia.org/wiki/Heuristic

Related – How to amplify noise in social media using other algorithms

https://decisionstats.com/2010/01/19/a-noisy-algorithm/

https://decisionstats.com/2015/04/14/random-thoughts-on-cryptography/

https://decisionstats.com/2013/12/14/play-color-cipher-and-visual-cryptography/

http://www.cs.cornell.edu/~shmat/shmat_oak09.pdf

De-anonymizing Social Networks
Arvind Narayanan and Vitaly Shmatikov
The University of Texas at Austin
Abstract
Operators of online social networks are increasingly sharing potentially sensitive information about users and their relationships with advertisers, application developers,
and data-mining researchers. Privacy is typically protected by anonymization, i.e., removing names, addresses, etc.
We present a framework for analyzing privacy and anonymity in social networks and develop a new re-identification algorithm targeting anonymized social- network graphs. To demonstrate its effectiveness on real-
world networks, we show that a third of the users who can be verified to have accounts on both Twitter, a popular microblogging service, and Flickr, an online photo-sharing site, can be re-identified in the anonymous Twitter graph with only a 12% error rate.