Further comments to the post of facial recognition earlier today http://blog.mydigitalfootprint.com/facial-recognition-is-it-part-of-my-digital-f

And other comments on the blog here http://blog.mydigitalfootprint.com/?sort=&search=facial

So the short version is that you can take a picture of someone, use the image to search images on say Facebook, match the two images using some sort of facial recognition algorithm and therefore find out someone's name (given the match and the different database entries).  This would apply even for someone just walking down the street, or tracking them where they walked, find you where they live and any other data.... shock.  It simple terms it is allowing databases to be paired to create value or make it really creepy.

Test cases prove it all works but they use data that was "selective" to make it quicker, but the reality is here and these services will be rolled out and you will be identified without being asked from your ID, which for some is the issue. “I want to hide”

As we realise and struggle with the fact that democracy entitles everyone who has a voice to be counted (freedom of expression), so we also realise that certain digital technology makes it harder to do harm.

Mr Policeman stops you;  "diving licence please" even for the trained eye and a data base detecting the forgery is hard but has real costs for Joe Public. However with the shoulder strap videocam the true person ID comes back allowing the  police to do his/ her job.  Where else can this tech be used that we would see value - Identification of people in a disaster, being identified as a VIP at your bank (assuming longevity of jobs has gone); criminal activity, names of guests, boarder control....

So all criminals will work this out and have ID photos on Facebook etc that are not them but similar enough to fool the algorithm or they wear a few facial extras, which makes them easy to identify!? 

But I still have the same question – who’s data is it?

BusinessWeek reports on retailers’ use of RetailNext camera surveillance software to glean intelligence from shoppers’ behaviour.  The software takes a video feed to analyze customer behaviour.  RetailNext is a real-time in-store monitoring, enabling retailers and manufacturers to collect, analyze and visualize in-store data.” 

RetailNext’s software can integrate data from hardware such as RFID chips and motion sensors to track customers’ movements.  The company explains that it “tracks more than 20 million shoppers per month by collecting data from more than 15,000 sensors in retail stores.”  Its service apparently helps stores figure out where to place certain merchandise to boost sales.  3VR is used by T-Mobile to track how people move around their stores, how long they stand in front of displays, and which phones they pick up and for how long.  3VR is testing facial-recognition software that can identify shoppers’ gender and approximate age.  

Radical point: facial recognition matches you via Google Image search (drop an image on the search bar) and links you in person, space and time to your digital persona.  So the question for you, how do you get access to your data that is not captured by you or used to sell you more,  but is used to improve design?    ….. Is this your data at all?

So it is reported that Amazon is guessing your faith/ religion from your gift wrap choice. Whilst Amazon is already the best at mining your data for recommendation and behavioural insights based on for shopping habits, location, music taste and who influence you – this one is an interesting slant. 

But what to they do with it, unless it helps with improving the recommendation and removing certain titles that could be offensive.

The question is therefore not about privacy but about who has the rights to filter what I see. This was explored at length in The Filter Bubble By Eli Pariser and whilst I did not agree with the entire book, the idea of someone’s code (and ethics) being my filter does worry me.

What can you do with data if you can collect it…

Acceleration, Vibration, Motion/Velocity/Displacement, Position/ location, Proximity, Pressure, Force/Strain/Load/Torque, Vertical Height above sea level, Sea/land motion activity, Movement

Water levels, Leaks, Chemical/Gas, Odour, Pollution, Biological process, Flow, Temperature/ heat production, Humidity/Moisture, Air pressure

Sound/ Ultrasound, Electric, Light, Solar intensity, Magnetic, Background noise, Signals

Images, Video, What is near by (saying hello), Routes and routines, key strokes, downloads, usage, content,

http://www.societyofrobots.com/sensors.shtml

source : http://www.adexchanger.com/

Source : http://smartdatacollective.com/wearecloud/40960/world-data-infographic?utm

SOURCES: Cisco; comscore; MapReduce, Radacti Group; Twitter; YouTube

A collaboration between GOOD and Oliver Munday, in collaboration with IBM.

Roger Grimes posted a very insightful blog about reuse of user ID and passwords, with the usual sprinkling of fairy dust and FUD to create sales for security experts, however it co-insides with Microsoft publishing some data about the reuse of passwords on different web sites and a very good research paper from INRIA in France which asked “How unique and traceable are usernames”

Essentially can identities established on multiple web sites be linked together based on the usernames to recreate an “identity” and what are the implications for privacy?  INRIA experiment looked at over 10 million usernames from popular services such as Google and eBay. In some of the tests, Google profiles that listed multiple accounts on different web services were used to establish “ground truth” about linked usernames.

The first finding was that the usernames chosen by people on the various websites tend to be very unique, with a probability of duplication being approximately one in one billion. This was true for a variety of web services, including a corporate network, Finnish web forums, and MySpace.

Second, the researchers found that when people used different usernames for different services, many of the usernames were constructed by making very small changes to existing usernames (e.g., sarah, sarah2).

Third, the study demonstrated that more than 50% of the usernames created for different services could be linked to one another because the username was identical, or very similar, and unique from other usernames.

Whilst privacy is a setting and you choice to limit the data about yourself on a case by case basis which each digital service (ebay, picasa, flickr, facebook, twitter, google, blogger, etc, if your profile can be linked to other services from other providers than it would appear to be feasible to build a more detailed personal profile from the various bits of partial information.

That being the theory someone quickly wrote a software application as a demonstration that theory has some justification. A quick examination of people using anonymous file sharing services (private BitTorrent trackers) found that 13 out of the 20 usernames examined could be linked to other web services (e.g., YouTube, eBay) and 4 usernames could be linked to real-world identities.

Two Sides

1.      Having everything linked could save you a lot of time and bring you value and so what these are not critical services (but I bet you use the same for banking…)  Google will do this for you (new service 17 Feb 2010) as part of their social search.

2.      Breach one, breach all.

Outcome

We need something better then Username and Passwords

Image from http://twitter.com/#!/STOP_IDFRAUDUK 

Google Latitude history is either very interesting or very scary.  In "my digital footprint" I wrote about an idea for a security application where a phone would close applications and services as you drifted off your normal routine based on history and location. Further it would also seek to find friends and contacts as additional support.

The latitude dashboard lets you see some graphs of how much time you spend at work, home, and out and about, and a list of your most visited places. You can also see a Google Map with your 500 latest updates added as pushpins.  From the dashboard, you can export your history of location updates as a KML file. It does ask you to opt in, and it doesn't share your location history with anyone. There is no doubt that it is a bit creepy as it gives step-by-step views of where you have been and even knows how many total miles I've travelled. You will love the little feature that explains how many miles you have travelled in terms of distance from the moon !

Be amazed, no need to build a unique application, it has been done. However, if you want a defence of where you phone was at the time of the murder, it would be great, proving you were with the phone is more difficult. 

If you want to really annoy everyone with what your are doing and where you are - check out http://www.footprintfeed.com/

Business Models

My digital footprint, as defined in this book, is about the system of collection, storage, analysis and value. The inputs to the system have focused on data types that can be collected as the user is willing to provide the data (explicit/active) and data that can be gathered by sensor.net (passive/automatic). It has already been stated that there is little value in the long run in collection (harvesting) and storing (regulated). There are possibly a few expectations to this, which are data types that are slow to replicate and can create a differential advantage by having/owning. There is a lot of value in the algorithm and good analysis tool. The understanding of value creation opportunities from analysis will create differential advantage. The outputs or value components are well understood in terms that they can be seen to create value. Additional value is created from the feedback loop as this provides a method to hone, focus and provide depth on responses to an individual based on their data inputs, and also the ability to add flavour, breadth and width based on the individual’s social graph. It has been explored who will engage and participate, and how to create this virtuous cycle and keep it going by understanding the bonds and bridges between risk, privacy and trust. This section focuses on the fundamental question of who owns the data and what the business models are that my digital footprint creates.

The models to exploit my digital footprint are determined by who owns the data, the options being: ‘I own my own data’ or ‘I give up my data’. Indeed, it is likely that, in many cases, both owning and sharing will be a healthy and amicable compromise, but it is worth focusing on the two separate models of ownership, as from this it is possible to draw clear intentions, value and models. Those that combine joint and shared ownership will, like the rainbow of trust, create and fill in the prime groups.

Figure 44 provides the outline of the models that will be explored.

Figure 44 Business models based on data ownership

What data can I own? 

I am in no doubt that owning data is difficult, ignoring the fact that even if I can get it, the analysis may be too difficult to create value. Some data (name, address, date of birth, certificates and other identity data as discussed at the outset) are in fact easy to own but hard to prove. Utility bills, bank and credit card statements are easy to collect and are easy to add to your database. Electronically collecting this data is easy and there are programmes that will allow you to build your own spend profile. Data from Amazon, eBay, PayPal, Yahoo, MSN, iTunes, SMS, email, AudioBoo, Palringo, Google, Facebook, Flickr, blog, Twitter, etc, is rather a more difficult case.

Yes, I replicate everything via a small widget on all my devices/screens so that I own a copy of my data (passive and active) and so does the service provider. It may be difficult to replicate some purchase information, especially cash and near field cashless. Currently the terms and conditions of many of these services determine that they have the rights over your data, they are currently prevented from sharing this to help you. How this small widget combines all the data streams is somewhat more difficult, as is how does my algorithm compare data from my social group to add colour and flavour to my services. Finally, how will my analysis output become available so that it can be fed into a service and, hence, I can enjoy the value?

Difficult and practical questions, and I am aware that some companies are working on them and why there is a lot of wealth to be created in this area and why the business model is wide open.

I purposely have not mentioned too many examples as the website http://www.mydigitalfootprint.com allows readers to add their own examples and promote their own services. I have also avoided the cross-subsidies, two side and freemium categorisation. In many cases, most of the economic models are available; it really depends on the motivation of the service provider, end customer and other parties on how they trade value for data.

I am aiming to gather views on collective action. Could/Should we as creators organise a tribe via Twitter and Google to recover ownership? Google own the link, but didn’t create it.

You can read the full chapter here

You can read the entire book here

You can buy the book here

Extract from “My Digital Footprint”, this is from the Chapter 6  “A two sided business model"

Be under no illusion that this (creating a virtuous circle) is either simple or easy. Mark Zuckerberg, the founder of Facebook, said when commenting on Beacon: “We’ve made a lot of mistakes building this feature, but we’ve made even more with how we’ve handled them. We simply did a bad job with this release, and I apologise for it.” This was in response to the 70,000 users on Facebook responding to Facebook’s new Ad and Beacon features in December 2007. The Facebook Ad followed a well-trodden path of purchase goods, PIN codes, getting free extras online. Fun and not a big fuss, Beacon, however, was different. Beacon would look at what you do and as such has deep roots in behavioural marketing based on targeting (open loop); however, it took your data and told your friends what you had done, really without any due care or thought. If you looked at someone’s profile, it told your friends. Rather hard to hide the fact you were looking for ‘fitties’. Buy a book or CD; well your friends may want it as well. In so many ways this is MY DIGITAL FOOTPRINT, but perhaps in the rush to monetise the knowledge, no rules were put in place. Ideas are cheap, implementation is hard.

At this point there is a need to bring back some themes that we skipped past in the early part of the book. These are the bonds and bridges between privacy, risk and trust. Figure 33 shows how these bonds and bridges relate to the MY DIGITAL FOOTPRINT feedback model of collection, store, analysis and value. The bonds and bridges of risk, privacy and trust are the connection fabric that ensures that the user continues to enjoy the experience or will cause the user to stop using the service. How do our experiences enforce the benefits that mean we participate more, or damage the benefits and head off into the dark side, causing lockdown and disengagement? Confidence, referral, recommendation, privacy, trust and risk are all key aspects to unlocking the virtuous circle.

Within this start phase of discovery there will no doubt be areas that will push boundaries. Whilst we understand legal and illegal as boundaries, there are often instances where within a grey area of illegal but acceptable, sometimes referred to as ‘socially acceptable’ [allowing your underage teenager to see a film with a different classification, providing your bank PIN to a partner when they jump out of the car in the rain to get some cash from an ATM, allowing your child to try alcohol at home], or ‘in the public interest’ in the case of journalism where the boundary is crossed, but no one would be too upset if the outcome is good, positive or beneficial. As the user is the provider and consumer of data, the damage will be caused when, as a trusted provider (brand), one crosses the line of protector to exploitation agent. A lot of work and debate is needed on unacceptable and inappropriate interpretation of data and Metadata. As a director, governance will be difficult, balancing these intangible issues along with wealth, competition and value, as this is where the opportunities lie; it is something I spend a lot of time worrying about.

To help in the understanding of why trust, privacy and risk are bonds and bridges, I am going to use the concept of capital (trust capital, risk capital and privacy capital) but not in the strict economics definition sense. Trust capital is how much, based on previous experience, you will be prepared to trust a service provider you have or have not previously used. Risk capital is how much, based on previous experience, you are prepared to risk (chance) using a service provider you have or have not previously used. Privacy capital is how much, based on previous experience, you are prepared to open up your privacy or personal data to a service provider you have or have not previously used. Capital, in each case, can be built and destroyed. In the next section we are going to briefly look at how you can build and erode capital.

The premise of privacy capital is that people will be born with no privacy capital and over one’s life this capital will be built or eroded based on experience. Good experiences of seeing your privacy protected or, in general, government protecting your privacy through action, law and regulation, the nation’s privacy standard will build privacy capital. Your social group, your family and their experiences will build privacy capital which is good. These good experiences provide positive feedback as shown in Figure 34. Positive experiences mean that you will have a higher propensity to engage and more hope by doing so that you will get better services and experiences. This lowers your fear, uncertainty and doubt (FUD). The more you see your privacy is protected, the more you may wish for someone to use your data as you do not fear them abusing it. You become more willing to share patterns, preferences, routes routines, shopping lists, click data, location and other inputs that will improve services. The converse being true; the more you see someone abuse your privacy, your friends, your social groups and generally the media invading privacy, the lower your FUD becomes, and your privacy capital is not eroded.

It is expected that your capital will even out at some point after you are 21 and will only be moved by major events.

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You can read the entire book here

You can buy the book here