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Category: General Thoughts

I took the #DeleteFacebook Challenge

In the last weeks, Facebook has been in the news a lot for its aggressive data gathering.  What has surprised me, is not that Facebook is in the news, but that it hasn’t happened much sooner.  Facebook is possibly the most invasive data gathering, privacy invading platform the world has ever seen, despite the fact that it is cloaked behind a veil of childish logos and thumbs up buttons.  Additionally, Facebook has engaged in some truly abhorrent practices, such as gathering text messaging and phone metadata from Android usersconducting secret psychological tests on over 700,000 users in 2012, ad programs that track users’ web activity off of Facebook, to say nothing of how Facebook was and most likely is being used to propagate fake news.

As someone who has worked in various regulated industries (banking, government) it appalls me how companies like Facebook abuse their users’ privacy.  My biggest issue is that Facebook disguises its data gathering efforts under a slick veneer of innocence which disguises their true intent.  Much like tobacco adverts of yore, Facebook and its “family” are targeted primarily towards younger people who don’t understand what they are giving up in exchange for the privilege of sharing their photos with their friends.

An extremely egregious example of this occurs on election days in the US.  Facebook will ask users a question: “Did you vote today?” and give you a little sticker on your profile if you answer that you did.  Now why do you think they would do that?  To encourage people to vote?  Hardly, though that may be a side benefit.  No, the real reason they do this is to gather information about people’s voting history, which Facebook then uses in their targeted political campaigns.  Don’t believe me?  You can read about it here: https://politics.fb.com.

The problem here is that Facebook doesn’t ask their users for consent in a way that a typical user will understand.  I am not trying to mock Facebook users, but most people who don’t work in data analytics, don’t really understand the implications of mass data gathering.  The image above is how Facebook Messenger asks for permission to gain access to your contacts, SMS and phone call logs. (Courtesy of ArsTechnica)  Nowhere in this image does it say anything about collecting SMS, phone logs or anything for that matter.  It looks cute and most people wouldn’t think twice about clicking on ok.

Silicon Valley’s Culture Needs to Change

The biggest issue I have with some of what Facebook has been caught doing is that enough of the company felt it was acceptable for them to do it.   That’s the bigger issue here.  Most likely, some manager at Facebook decided, why don’t we gather all our Android users’ text data and mine it!  And nobody said a bloody thing. No leaks to the news media, no disgruntled employees writing blog posts about it, nothing….  Which ultimately means that everyone involved felt it was totally acceptable to take their users’ SMS and phone logs.   This practice only ended when Android disabled the functionality, so it wasn’t as if Facebook execs had some crisis of conscious.

But, I’m a realist.  Facebook’s revenue is generated by selling targeted advertising and the way it targets its ads is by gathering data about its audience.  Whilst Mr. Zuckerberg can write pithy non-apologies about it, nothing will change because this is how Facebook makes money.  The only way this changes, is for people like you to get off of Facebook (and Instagram, and WhatsApp) in significant numbers and for advertisers to stop spending money on Facebook ads. As long as there is a market for this data, the sad reality is that there will be more and more companies trying to invade your privacy and sell it to the highest bidder.

Educate Yourself About How Companies Monetize Your Data

You need to understand how companies are using your data and make a conscious choice about whether that company provides enough value to justify that loss in privacy.  Frankly, this is why I prefer using companies whose primary revenue stream is not derived from data monetization.  This is why I choose to use iPhones instead of Android, iMessage instead of WhatsApp, socializing with real friends instead of Facebook.  You can generally tell this is the case by whether you have to pay for a service.  Generally speaking, companies which charge for their services are not looking to invade your privacy to the same degree as companies that offer their services “for free”.  As the saying goes: “If you aren’t paying for it, YOU are the product.

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A New Threat: Stalkerware

What would you do if you attended a political event or protest and the next day, you receive targeted adverts for that political cause?  Would that be cause for concern?  After all, you don’t post about your political views, how did the advertisers know?  You didn’t sign any rosters or register, so how did they know you were there?

I recently became aware of a new category of computer-evil: stalkerware.  I thought I was being clever and would have the privilege of coining a new term, but a few other people have already coined the term.  However, I would like to propose a slightly different definition.  In an article originally appearing on Motherboard, stalkerware is defined as:

Stalkerware is defined as invasive applications running on computers and smartphones that basically send every bit of information about you to another person. This covers the gamut from programs that can be purchased online to give third parties access to basically everything on your computer from photos, text messages and emails to individual keystrokes, to apps that activate your Mac’s webcam without your knowledge.

I’m not really seeing the difference between this definition and “traditional” spyware, but stalkerware as I define it is:

Software that automatically reports your location on a regular basis without your knowledge or consent.

The stalkerware that Motherboard writes about are dedicated programs or apps that someone deliberately installs on a target’s mobile device in order to track their activity for whatever reason.  Stalkerware as I define it is a little different, in that it is not targeted at one individual.  These are applications that are installed on mobile devices that track your every move–literally stalking you–most likely without your knowledge.

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Thoughts on Teaching Data Science

A big interest of mine is how to impart what little I know of the tools and techniques of data science to others.  When I was at Booz Allen, I taught numerous classes both for internal staff and for various clients.  I’ve also taught for Metis, O’Reilly Publishing and for the last three years, at BlackHat so I do have some experience in the matter.   I’ve looked at MANY data science programs to see if what they are teaching lines up what I’m teaching and I’d like to share some things which I’ve noticed which will hopefully help you build a better data science program.  My goal here is to share my mistakes and experiences over the years and hopefully if you are building a data science training program, you can learn from what I learned the hard way.  I make no claims to be the perfect data science instructor, and I’ve made plenty of mistakes along the way.

While I’m at it, I’ll put in a plug for an upcoming data science class which I am teaching with Jay Jacobs of BitSight Security at the O’Reilly Security Conference in NYC, October 29-30th.

Really, data science instruction is an optimization problem: as an instructor, your goal is to minimize confusion whilst maximizing understanding.  To do this, you must remove as many obstacles as possible from the students’ path which create dissonance.  This may seem silly, but I have observed that if you have small errata in your code, or your code doesn’t work on their machine, even due to something they did, it significantly detracts from their learning experience and their opinion of you as an instructor.  Therefore, removing all these obstacles to understanding is vital to your success as an instructor.

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The Difference between Software Development and Data Science

I am fortunate enough to get regular messages from recruiters on LinkedIn asking to speak with me about software development jobs.  Here’s the thing… I’m not a software developer, I do data science and data analytics.  For the last seven years, my job title has included the words “data” and “scientist” in the title.  I have never held a position with the words “Software” and “Developer” in the title.  I have taught and am currently teaching classes with titles such as “Data Science for Security Professionals” and “Applied Data Science for Security”.   All of this is on my LinkedIn profile, yet despite this, the messages continue.

On some level, it makes sense.  If you look at my resume, you’d see that I have a degree in computer science, experience with various coding languages, and projects on github.  Hell, I’m a committer for Apache Drill…

So what’s the difference between a data scientist and software developer?

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Academics and Data Science

I received the following comment on an article: Let’s Stop Using the Term Fake Data Scientist and thought it merited a response.  Usually the comments I receive are constructive even if they disagree with what I wrote, but this particular comment, demonstrated an arrogance which I believe is a huge problem in the data science world.

You can of course read the original article here, but the basic point was that data science is interdisciplinary field–consisting of a mixture of computer science, applied mathematics, and subject matter expertise, with a smattering of data visualization and communication skills.   I believe that it is inappropriate to label someone as a fake simply because their skillset is proportioned differently than many math-centric data scientists.  I’m also a believer in Dr. Carol Dweck’s thesis on having a growth-oriented mindset (as stated in her book Mindset) and that people who might be working in data science but whose skills need development in a certain area, should be given instruction and assistance rather than derogatory labels.

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The End of Privacy As We Know It

In the news on Friday I saw a series of articles about a recent change in communication rules which was rejected by the Senate that would have prohibited ISPs from selling your browsing histories.  I understand why ISPs would want to monetize this data, after all, this data would be extremely valuable to online advertisers to more accurately serve ads.  But I think it should give us pause to ask the question is this in fact ethical?

While there really is no 1 to 1 comparison, the closest thing(s) would be either the telephone company selling your call records, or the post office (or other courier services such as UPS) aggregating and selling the information on the outside of your mail.  I would strongly suspect that most people, if asked, would certainly not want their communication records sold to the highest bidder and yet that is precisely what Congress is allowing.

What Does This Mean for Privacy?

If ISPs are allowed to sell your browsing histories, I don’t believe that it is overstating things to say that this represents the end of privacy on the internet.  While we didn’t have much privacy on the internet any these days anyway, but if the ISPs are allowed to sell browsing records, it’s pretty much over.

With that said, it is difficult to discern exactly what is going to be allowed under the new rule change, but if I’m reading the news articles correctly it will allow ISPs to sell records of metadata of your web browsing.  To a competent analyst, this data would be a virtual gold mine for targeted advertising and all sorts of other services, none of which are really beneficial to the individual.   As I’ve shown in my Strata talks about IoT data, (here and here) if you gather enough seemingly innocuous data about an individual, it is entirely possible to put together a very accurate picture of their life.  From my own experience, if you were to look at my browsing history for a few months, you could very easily determine things like when my bills are due, what companies I do business with, when I go to work/bed, what chat services I use, things I may be interested in buying, what places I’m interested in visiting, etc.  The bottom line is that I consider my web browsing to be personal.  I don’t want to share that with anyone, not because I have something to hide, but rather because I want the choice.  I see no benefit whatsoever to the consumer in this rule change.

What can you do to protect your privacy?

Unfortunately, there really aren’t a lot of options.  From the technical perspective, there are several technical options–none great–to preserve your privacy.  It is not possible to keep the ISPs from getting your data, but you can make that data useless with TOR and VPNs.

  • Virtual Private Network (VPN):  VPNs have been traditionally used by corporations to allow remote access into private networks using the public internet.  VPNs create a secure tunnel between your computer and a proxy server then your web traffic passes through that server–which can be anywhere in the world.  For those of you who don’t work for large corporations, there are free and paid VPNs that you can use to access the web, however, I would avoid any free VPN service as they are likely making money by, you guessed it, collecting web traffic and analyzing it.   VPNs may seem like an ideal countermeasure, however there are issues with VPNs as well.  For starters, you are adding bottlenecks and complexity and hence losing speed.  Secondly many sites–particularly sites that have geographically based licensing such as Netflix–block traffic from VPNs.   VPNs don’t make you anonymous but they can make your data much more difficult to collect.
  • TOR:  TOR stands for The Onion Router (https://en.wikipedia.org/wiki/Tor_(anonymity_network)) and it is similar to a VPN but instead of using one proxy server, TOR uses a series of encrypted relays and makes traffic much more difficult.  TOR has been used in many countries to successfully evade internet censorship.  TOR has the added benefit of allowing anonymous browsing, however, it does introduce additional complexity into your browsing.  There also is a speed penalty for using TOR and you will find that you will not be able to access certain services using TOR.

Depending on how protective of your privacy you are, this may or may not matter, but it is important to understand that when using these technologies, guaranteeing your privacy depends on properly configuring them.  One small misconfiguration can expose your personal data.

I should also mention here that the so-called privacy modes that most browsers include do absolutely nothing to protect your privacy over the network.  Privacy mode erases your browsing history and cookies on your local machine, but you are still vulnerable to snooping over the network.

What else can I do?

This rule change represents a complete failure of government to do the thing it is really supposed to do–protecting the rights of its citizens.  It’s sad that the whole world was up in arms in response to Snowden’s revelations, and yet the silence is deafening in response to unlimited, widespread corporate surveillance.  Indeed, you have to read the hacker blogs (and my site) to find any kind of discussion of this issue.  This story got virtually zero coverage in the news media.

What is a real shame is that this appears to have become a partisan issue in that the vote in the Senate was a strict party-line vote.  It is entirely possible that the new Congress voted to repeal these rules simply because they were put in place by the previous administration.

At this point, the government is not looking out for its citizens’ interests in this regard and therefore it is upon individual citizens to take action to preserve our privacy.  In addition to the technical measures listed above here are some suggestions for what you can do:

  1. Contact your Congressional Representative(s) and Senator(s):  The Congressional switchboard number is 202-224-3121.  Always be courteous, professional and polite when speaking with Congressional Staff.   Be sure to convey why you are calling.  While it is unlikely that you will speak directly to your Senator or Congressman, their Staff have enormous influence and you should be respectful to them.  Make it clear that you do not welcome corporate surveillance.
  2. Educate Others:  I suspect that the reason this received so little attention is that the average person doesn’t really understand security, privacy and the consequences of this kind of data collection.  Therefore, it is incumbent upon those of us who work in data analytics and security to explain the implications of these policies in an understandable manner to non-technical people.

I would strongly urge everyone to do what they can to protest this rule change.  If we do nothing, we might wake up one day and find that our online privacy has ceased to exist.

 

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Doing More with IP Addresses

IP addresses can be one of the most useful data artifacts in any analysis, but over the years I’ve seen a lot of people miss out on key attributes of  IP addresses to facilitate analysis.

What is an IP Address?

First of all, an IP address is a numerical label assigned to a network interface that uses the Internet Protocol for communications.  Typically they are written in dotted decimal notation like this: 128.26.45.188.  There are two versions of IP addresses in use today, IPv4, and IPv6.  The address shown before is a v4 address, and I’m going to write the rest of this article about v4 addresses, but virtually everything applies to v6 addresses as well.  The difference between v4 and v6 isn’t just the formatting.  IP addresses have to be unique within a given network and the reason v6 came into being was that we were rapidly running out of IP addresses!  In networking protocols, IPv4 addresses are 32bit unsigned integers with a maximum value of approximately 2 billion.  IPv6 increased that from 32bit to 128 bits resulting in 2128 possible IP addresses.

What do you do with IP Addresses?

If you are doing cyber security analysis, you will likely be looking at log files or perhaps entries in a database containing the IP address in the dotted decimal notation.  It is very common to count which IPs are querying a given server, and what these hosts are doing, etc.

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Thoughts and Goals for the Upcoming Year

It’s been an interesting year both career wise and generally.  This last year, I’ve had the amazing opportunity to speak at numerous conferences around the world, as well as give classes all over the world in data science and Apache Drill.  I’ve also learned a lot about the internals of Drill and even contributed to the codebase.  With that said, one can never rest on one’s laurels and as such I have a lot in store for the year.

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A Data Scientist’s Perspective on the Election and What Went Wrong

I originally drafted a version of this article in August, but decided not to post it because I didn’t want my blog to be political commentary.  However, given the shocking election results and the epic failure of the political polling/predictive analytics industries I couldn’t resist sharing my thoughts on the matter.  As a data scientist, I have been watching this election with a lot of anticipation and curiosity.  Back in August, my original draft was entitled “What happens to Data Science if Trump wins?”  and in it, I wrote some thoughts about what the impact would be to the data science world if Trump won.  The main premise was that a Trump victory would be disruptive in how political campaigns are run and most importantly, how the analytics used to measure political campaigns would be called into question.  I also thought that the value of the super-creepy targeted advertising that Facebook and other social media sites are using might get called into question.  But more on that later…  Lastly, I’m attempting to write this article without infusing my own political opinions into the central arguments.  If I am successful, the reader will have no idea what my political views are.

Ultimately, there are two questions which need answers:

  1. How is it that nearly every reputable news source and polling agency incorrectly predicted the election results?
  2. How can data science be used to avoid repeated errors of this scale?

The first point really bears some fleshing out.  It wasn’t just that everyone predicted a Clinton victory, it was that nearly every source–including the vaulted Nate Silver–predicted a massive Clinton victory.

For this discussion, I will presuppose–perhaps naively–that the pollsters and other political analytic professionals are not themselves biased and are in fact trying to give an accurate prediction as possible and not allowing their own opinions about the candidates influence their analysis.  With that said, I hypothesize that this election was a perfect storm of polling biases, groupthink, and poor use of data that in the end resulted in the massive failures that occurred on election day.

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The Biggest Problem in Data Science and How to Fix It

Imagine you have some process in your organization’s workflow that consumes 50%-90% of your staff’s time and contributes no value to the end result.  If you work in the data science or data analytics fields you don’t have to imagine that because I’ve just described what is, in my view, the biggest problem in advanced analytics today: the Extract/Transform/Load (ETL) process.  This range doesn’t come from thin air.  Studies from a few years ago from various sources concluded that data scientists were spending between 50%-90% of their time preparing their data for analysis.  (Example from Forbes, DatascienceCentral, New York Times) Furthermore, 76% of data scientists consider data preparation the least enjoyable part of their job, according to Forbes.

If you go to any trade show and walk the expo halls, you’ll see the latest whiz-bang tools to “leverage big data analytics in the cloud”, and you’ll be awed by some amazing visualization tools.  Many of these new products can do amazing things… once they have data, which brings us back to our original problem…that in order to use the latest whiz-bang tool you still have to invest considerable amounts of time in data prep.  The tools seem to skip that part, and focus on the final 10% of the process.

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