I’ve rallied for years against the use of PII (or Personally Identifiable Data) as unhelpful in the privacy sphere. This term is used is some US legislation and has unfortunately made its way into the vernacular of the cyber-security industry and privacy professionals. Use of the term PII is necessarily limiting and does allow organizations to see the breadth of privacy issues that may accompany non-identifying personal data. This post is meant to shed light on the nuances in different types of data. While I’ll reference definitions found in the GDPR, this post is not meant to be legislation specific.

Personal Data versus Non-personal Data

The GDPR defines Personal Data as “means any information relating to an identified or identifiable natural person (‘data subject’); an identifiable natural person is one who can be identified, directly or indirectly, in particular by reference to an identifier such as a name, an identification number, location data, an online identifier or to one or more factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.” The key here term in the definition is the phrase “relating to.” This broad refers to any data or information that has anything to do with a particular person, regardless of whether that data helps identify the person or that person is known. This contrast with non-personal data which has no relationship to an individual.

Personal Data: “John Smith’s eyes are blue.”

In this phrase, there are three pieces of personal data. The first is the name John which is a first name related to an individual, John Smith. The second is his last name. Finally, the third is blue eyes, which also relates to John Smith.

Anonymous Data: “People’s eyes are blue.”

No personal data is indicated in the above sentence as the data doesn’t relate to an individual, identified or identifiable. It relates to people in general.

Identified Data versus Pseudonymous Data

Much consternation has been exhibited over the concept of pseudonymized data. The GDPR provides a definition of pseudonymized: “means the processing of personal data in such a manner that the personal data can no longer be attributed to a specific data subject without the use of additional information, provided that such additional information is kept separately and is subject to technical and organisational measures to ensure that the personal data are not attributed to an identified or identifiable natural person.” The key phrase in this definition is that data can no longer be attributed to an individual without additional data. Let me break this down.

Identified Data: “John Smith’s eyes are blue.”
The same phrase we used in our example for Personal Data is identified because the individual, John Smith, is clearly identified in the statement.

Pseudonymous (Identifiable) Data: “User X’s eyes are blue.”
Here we have processed the individual’s name and replaced it with User X. In other words, its been pseudonymized. However, it is still identifiable. From the definition above, Personal Data is data relating to an identified or identifiable individual. Blue eyes are still related to an identifiable individual, User X (aka John Smith). We just don’t know who he is at the moment. Potentially we can combine information that links User X to John Smith. Where some people struggle is understanding there must be some form of separation between the use of the User X pseudonym and User X’s underlying identity. Store both in one table without any access controls and you’ve essentially pierced the veil of pseudonymity. WARNING: Here is where it can get tricky. Blue eyes are potentially identifying. If John Smith is the only user with blue eyes, it makes it much easier to identify User X as John Smith. This is huge pitfall as most attributable data is potentially re-identifying when combined with some other data.

Identifying Data versus Attributable Data

In looking at the phrase “John Smith’s eyes are blue” we can distinguish between identifying data and attributable data.

Identifying Data: “John Smith”
Without going into the debate of number of John Smiths in the world, we can consider a person’s name as fairly identifying. While John Smith isn’t necessarily uniquely identifying, a type of data, a name, can be uniquely identifying.

Attributable Data: “blue eyes”
Blue eyes is an attribution. It can be attributable to a person, in the case of our phrase “John Smith’s eyes are blue.” It can be attributable to a pseudonym: “User X’s eyes are blue.” As we’ll see below, it can also be attributed anonymously.

Anonymous Data versus Anonymized Data

GDPR doesn’t define anonymous data but in Recital 26 it says “anonymous information, namely information which does not relate to an identified or identifiable natural person or to personal data rendered anonymous in such a manner that the data subject is not or no longer identifiable.” In the first example, I distinguished Personal Data with Anonymous Data, which didn’t relate to specific individual. Now we need to consider the scenario where we have clearly Personal Data which we anonymize (or render anonymous in such a manner that the data subject is not or no longer identifiable).

Anonymous Data: “People’s eyes are blue.”
For this statement, we were never talking about a specific individual, we’re making a generalized statement about people and an attributed shared by people.

Anonymized Data: “User’s eyes are blue.”
For this statement, we took Identified Data (“John Smith’s eyes are blue”) and processed in a way that is potentially anonymous. We’ve now returned to the conundrum presented with Pseudonymous data. Specifically, if John Smith is the only user with blue eyes, then this is NOT anonymous. Even if John Smith is a one of a handful of users with blue eyes, the degree of anonymity is fairly low. This is the concept of k-anonymity, whereby a particular individual is indistinguishable from k-1 other individuals in the data set. However, even this may not given sufficient anonymization guarantees. Consider a medical dataset of names, ethnicities and heart condition. A hospital releases an anonymized list of heart conditions (3 people with heart failure, 2 without). Someone with outside knowledge (that those of Japanese descent rarely have heart failure and the names of patients) could make a fairly accurate guess as to which patients had heart failure and which did not. This revelation brought about the concept of l-diversity in anonymized data. The point here is that unlike Anonymous Data which never related to a specific individual, Anonymized Data (and Pseudonymized Data) should be carefully examined for potential re-identification. Anonymizing data is a potential minefield.

If you need help navigating this minefield, please feel free to reach out to me at Enterprivacy Consulting Group

I had the pleasure of serving last week on a panel at the Privacy and Security Forum with privacy consultant extraordinaire Elena Elkina and renowned privacy lawyer Mike Hintze. The topic of the panel was Good Bots and Bad Bots: Privacy and Security in the Age of AI and Machine Learning. Serendipitously, on the plane to D.C. earlier that morning someone had left a copy of the October issue of Wired Magazine, the cover of which displayed a dark and grim image of Ryan Gosling, Harrison Ford, Denis Villeneuve, and Ridley Scott from the new dystopian film, Blade Runner 2049. Not only was this a great intro to the idea of bot (in the movie’s case human like androids) but the magazine contained two pertinent articles to our panel discussion: “Q: In. Say. A customer service chat window, what’s the polite way to ask whether I’m talking to a human or a robot?” and “Stop the chitchat: Bots don’t need to sound like us.” Our panel dove into the ethics and legality of deception, in say a customer service bot pretending to be human.

White the idea was fresh in my mind, I wanted to take a moment to replay some of the concepts we touch upon for a wider audience and talk about the case study we used in more detail than the forum allowed. First off, what did we mean by bots? I don’t claim this is a definitive definition but we took the term, in this context, to mean two things:

• Some form of human like interface. This doesn’t mean they have to the realism of Replicants in Blade Runner, but some mannerisms in which a person might mistake the bot for another person. This goes back to the days, as Elena pointed out, of Alan Turing and his Turing test, years before any computer could even think about passing. (“I see what you did there.”). The human like interface potentially has an interesting property, are people more likely to let their guard down and share sensitive information if they think they are talking to another person? I don’t know the answer to that and their may be some academic research on that point. If their isn’t I submit that it would make for some interesting research.
• The second is the ability to learn and be situationally aware. Again, this doesn’t require the super sophistication of IBM’s Watson but any ability to adapt to changing inputs from the person with whom it is interacting. This is key, like the above, to giving the illusion a person is interacting with another person. By counter example, Tinder is littered with “bots” that recite scripts with limited, if any, ability to respond to interaction.

Taxonomy of Risk

Now that we have a definition, what are some of the heightened risks associated with these unique characteristics of a bot that, say, a website doesn’t have? I use Dan Solove’s Taxonomy of Privacy as my goto risk framework. Under the taxonomy I see 5 heightened risks:

1. Interrogation (questioning or probing of personal information): In order to be situationally aware, to “learn” more, a bot may ask questions of someone. Those questions could go too far. While humans have developed social filters, which allows us to withhold inappropriate questions, a bot lacking a moral or social compass could ask questions which make the person uncomfortable or is invasive. My classic example of interrogation is an interview where the interviewer asks the candidate if they are pregnant or planning to become pregnant. Totally inappropriate in a job interview. One could imagine a front like recruitment bot smart enough to know that pregnancy may impact immediate job attendance of a new hire but not smart enough to know that it’s inappropriate to ask that question (and certainly illegal in the U.S. to use pregnancy as a discriminatory criteria in hiring).
2. Aggregation (combining of various piece of personal information): Just as not all questions are interrogations, not all aggregation of data creates a privacy issue. It is when data is combined in new and unexpected ways, resulting in information disclosure than the individual didn’t want to disclose. Anyone could reasonably assume Target is aggregating sales data to stock merchandise and make broad decisions about marketing, but the ability to discern pregnancy of a teenager from non-baby related purchased was unexpected, and uninvited. For a pizza ordering bot, consider the difference between knowing my last order was a vegetable pizza and discerning that I’m a vegetarian (something I didn’t disclose) because when I order for one its always vegetable but if I order for more than one, it includes meat dishes.
3. Identification (linking of information to a particular individual): There may be perfectly legitimate reasons a bot would need to identify a person (to access that person’s bank account for instance) but identification as an issue comes into play when its the perception of the individual that they would remain anonymous or at the very least pseudonymous. If I’m interacting with a bot as StarLord1999 and all the sudden it calls me by the name Jason, I’m going to be quite perturbed.
4. Exclusion (failing to let an individual know about the information that others have about her and participate in its handling or use): As with aggregation, a situationally aware bot, pulling information from various sources may alter its interaction in a way that excludes the individual from some service without the individual understanding why and based on data the individual doesn’t know it has. For instance, imagine a mortgage loan bot, that pulls demographic information based on a user’s current address, and steers them towards less favorable loan products. That practice sounds a lot like red-lining and if it has discriminatory effects, could be illegal in the U.S.
5. Decisional Interference (intruding into an individual’s decision making regarding her privacy affairs): The classic example I use for decisional interference is China’s historic one-child policy which interferes with a family’s decision making on their family make-up, namely how many children to have. So you ask, how can a bot have the same effect? Note the law is only influential, albeit in a very strong way. A family can still physically have multiple children, hide those children or take other steps to disobey the law, but the law is still going to have a manipulatory effect on the decision making. A bot, because if it’s human interface, and advanced learning and situational knowledge, can be used to psychologically manipulate people. If the bot knows someone is psychologically prone to a particular type of argument style (say appealing to emotion) it can use that and information at it’s disposal to subtly persuade you towards a certain decision. This is a form of decisional interference.

Architecture and Policy

I’m not going to go into a detailed analysis of how to mitigate these issues, but I’ll touch on two thoughts: first, architectural design and second, public policy analysis. Privacy friendly architecture can be analyzed along two axes, identifiability and centralization. The more identified and more centralized the design, the less privacy friendly it is. It should be obvious that reducing identifiability reduces the risk of identification and aggregation (because you can’t aggregate external personal data from unidentified individuals) so I’ll focus here on centralization. Most people would mistakenly think of bots as being run by a centralized server, but this is far from the case. The Replicants in Blade Runner or “autonomous” cars are both prominent examples of bots which are decentralized. In fact, it should be glaringly apparent that a self-driving car being operated by a server in some warehouse introduces unnecessary safety risks. The latency of the communication, potential for command injections at the server or network layer, and potential for service interruption are unacceptable. The car must be able to make decisions immediately, without delay or risk of failure. Now decentralization doesn’t help with many of the bot specific issues outlined above, but it does help with other more generic privacy issues, such as insecurity, secondary use and others.

Public policy analysis is something I wanted to introduce with my case study during the interactive portion of the session at the Privacy and Security Forum. The case study I present was as follows:

Kik is a popular platform for developing Bots. https://bots.kik.com/#/ Kik is a mobile chat application used by 300 million people worldwide and an estimated 40% of US teens at one time or another have used the application. The National Suicide Prevention Hotline, recognizing that most teens don’t use telephones wants to interact with them in services they use. The Hotline wants to create a bot to interact with those teens and suggest helpful resources. Where the bot recognizes a significant risk of suicide rather than just casual inquiries or people trolling the service, the interactions will first be monitored by a human who can then intervene in place of the bot, if necessary.

I’ll highlight one issue, decisional interference, to show why it’s not a black and white analysis. Here, one of the objectives of the service and the bot, is to prevent suicide. As a matter of public policy, we’ve decided that suicide is a bad outcome and we want to help people who are depressed and potentially suicidal get the help they need. We want to interfere with this decision. Our bot must be carefully designed to promote this outcome. We don’t want the bot to develop in a way that doesn’t reflect this. You could imagine a sophisticated enough bot going awry and actually encouraging callers to commit suicide. The point is, we’ve done that public policy analysis and determined what the socially acceptable outcome is. Many times organizations have not thought through what decisions might be manipulated by the software they create and what the public policy is that should guide they way the influence those decisions. Technology is not neutral. Whether it’s is decisional interference or exclusion or any of the other numerous privacy issues, thoughtful analysis must precede design decisions.

As many of you probably know, the Lesser Antilles in the Caribbean were battered this week by Hurricane Irma, one of, if not, the most powerful storms to hit the Caribbean. Barbuda was essentially annihilated (90% of building damaged or destroyed). Anguilla, a country of only 11,000, hasn’t fared too well either.

What some, but not all, in the Financial Cryptography/crypto-currency community may not know is that Anguilla was home to the first and several subsequent International Financial Cryptography Conferences from 1997. The association, IFCA, still bears that legacy with the TLD of .ai, ifca.ai. The paper presented at IFCA conference and the discussions held by many of its participants paved the ground for Tor, Bitcoin and the crypto-currency revolution. Eventually, the conference had grown to big for this tiny island.

More directly, two of the founders of that conference, Bob Hettinga and Vince Cate, currently live on Anguilla.  While they have since separated from IFCA, their early evangelism was instrumental in the robust, thriving and important work developed at the conference throughout the years. Bob and Vince were both directly affected by the hurricane, though not their spirit. Here is a quote  from Bob about the aftermath:

Have a big wide racing stripe of missing aluminum from front to back. Sheets of water indoors when it rains. Gonna tarp it over tomorrow though. Sort of a rear vent across the back of the great room. See the videos. Blew out three steel accordion shutters covering three sliding glass doors. 200mph winds? No plywood on the island, much less aluminum.

Airport has no tower anymore. SXM airport had its brand new terminal demolished. Royal Navy tried to land at the commercial pier but the way was blocked. Rum sodomy and the lash. 🙂

We need money of course, blew a bunch prepping, etc. No reserves to speak of even then. Gotta pay people to do a bunch of stuff, but the ATMs are down so people are just doing shit anyway. Works in our favor except to buy actual stuff. We hadda a little cash stashed in the house and paid about half of that boarding up three of the six demolished sliding glass doors. Tomorrow we’ll put the MICR numbers off one of [Mrs RAH’s] checks here, so you can wire us money if you want. Heh. It still might be weeks before we get it. Or days. Just no idea right now.

If we can get the materials replacing the aluminum on the roof can be done in a couple days. Getting it here under normal circumstances would take weeks.

Certainly months now.

Then we have to rip out and replace all the Sheetrock in the house, basically all the closets and bathrooms and the inner bedroom walls. Again days to fix, months to do.

All the gas pumps on the island were flattened. Literally blown away. That makes thing interesting. A couple grocery stores are open, but who knows how long there inventories will hold up.

See “Royal Navy” above.

Both cisterns fine. 38000 gallons total. Full. Wind ripped off the gutters so no discernible salt in the water. And little floating litter on top. We’ve got water, and we can pull buckets out of the cisterns until the cows come home. I’d been looking at building a hand pump out of PVC and check valves, and now I have some incentive.

Car needs brake work. Threw the error codes the day after the storm. Supposed to park it and have the shop come and get it. Heh. No shop anymore. No money so it works out. 🙂 Car exists to charge the smartphones at the moment. Full tank. Life is good

See the rain from inside Bob’s house during the Hurricane.

I’m making this post to request that the community support Bob, Vince and the island of Anguilla. We can’t help the entire Caribbean but we can donate to efforts to support them and our adoptive island of Anguilla.

For donations to support the island of Anguilla, please consider donating to the Help Anguilla Rebuild Now fund or individual recipients on this page

To help Bob or Vince, please contact them directly (or you can contact me). I will update this page with funding options as I’m made aware of them.

My contact information:

Email: rjc at privacymaverick

Twitter @privacymaverick

LinkedIn

Financial privacy is most often conceptualized in terms of the confidentiality of one’s finances or financial transactions. That’s the “secrecy paradigm,” whereby hiding money, accounts, income, expenses prevents exposure of one’s activities, subjecting one to scrutiny or revealing tangential information one’s wants to keep private. Such secrecy can be paramount to security as well. Knowing where money is held, where it comes from or where it goes give thieves and robbers the ability to steal that money or resource. Even knowing who is rich and who is poor helps thieves select targets.

Closely paralleling “financial privacy as confidentiality,” is identity theft, using someone else’s financial reputation for one’s own benefit. In the US, Graham-Leech Bliley’s Safeguards Rule provides some prospective protection against identity theft, while the Fair Credit Reporting Act (FCRA), Fair and Accurate Credit Transactions Act (FACTA) and the FTC’s Red Flag Rules provide additional remedial relief.

However, as I’m found of saying in my Privacy by Design Training Workshops, “that’s not the privacy issue I want to talk about now.” All of the preceding examples are issues of information privacy. Privacy, though, is a much broader concept than that of information. In his Taxonomy of Privacy, Professor Daniel Solove categorized privacy issues into four groups: information collection, information processing, information dissemination and invasions. It’s that last category to which I turn the reader’s attention. Two specific issues fall under the category of “invasions,” namely intrusion and decisional interference. Intrusion is something commonly experienced by all when a telemarketer calls, a pop-up ad shows up in your browser window, you receive spam in your inbox or a Pokemon Go player shows up at your house; it is the disturbance of one’s tranquility or solitude. Decisional interference, may be a more obscure notion for most readers, except for those familiar with US Constitutional Law. In a series of cases, starting with Griswold v. Connecticut and more recently and famously in Lawrence v. Texas, the Supreme Court rejected government “intrusion” in the decisions individual’s make of their private affairs, with Griswold concerning contraceptives and family planning and Lawrence concerning homosexual relationships. In my workshop, I often discuss China’s one child law as a exemplary of such intrusion.

The concept of decisional interference has historical roots in US privacy law. Alan Westin’s definition of information privacy (“the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others”) includes a decisional component. Warner and Brandeis’ right “to be let alone” also embodies this notion of leaving one undisturbed, free to make decisions as an autonomous individual, without undue influence by government actors. With this broader view, decisional interference need not be restricted to family planning, but could be viewed as any interference with personal choices, say government restricting your ability to consume oversized soft drinks. I guess that’s why my professional privacy career and political persuasion of libertarianism are so closely tied.

But is decisional interference solely the purview of government actors, then? Up until recently, I struggled with coming up with a commercial example of decisional interference, owing to my fixation on private family matters. A recent spark changed that. History is replete with financial intermediaries using their position to prevent activities they dislike and since many modern individual decisions involve spending money, the ability of an intermediary to disrupt a financial transaction is a form of decisional interference. A quick look at Paypal’s Acceptable Use Policy provides numerous examples of prohibited transactions that are not necessarily illegal (which is covered by the first line of their AUP). Credit card companies have played moral police, sometimes but not always at the behest of government, but more often being overly cautious, going beyond legal requirements. Even a financial intermediaries’ prohibition on illegal activities is potential problematic, as commercial entities are not criminal law experts and will choose risk-averse prohibition more often than not, leading to chilling of completely legal, but financially dependent, activity.

This brings me to the subject of crypto-currencies. Much of the allure of a decentralized money system like Bitcoin is not in financial privacy vis-a-vis confidentiality (though Zerocoin provides that) but in the privacy of being able to conduct transactions without inference by government or, importantly, a commercial financial intermediary. What I’m saying is not a epiphany. There is a reason that Bitcoin beget the rise of online dark markets for drugs and other prohibited items. Not because of the confidentiality of such transactions (in fact the lack of confidentiality played into the take-down of the Silk Road) but because no entity could interfere with the autonomous decision making of the individual to engage in those transactions.

Regardless of your position on dark markets, you should realize that in a cashless world, the ability to prevent, deter or even discourage financial transactions is the ability to control a societ. The infamous pizza privacy video is disturbing not just because of the information privacy invasions but because it is attacking the individual’s autonomy in deciding what food they will consume by charging them different prices based on external intermediaries social control (here a national health care provider). This is why a cashless society is so scary and why cryptocurrencies are so promising to so many. It returns financial privacy of electronic transaction vis-a-vis decisional autonomy to the individual.

[Disclosure: I have an interest in a fintech startup developing anonymous auditable accountable tokens which provides the types of financial privacy identified in this post.]