How to implement an anonymous data collection scheme that allows the manufacturer to anonymously collect data from its end devices without knowing exactly which device it came from? Yes, this is one of the challenges for the second Blockchain Hackathon (part of LongHash Cryptocon Vol2) in Berlin on May 18-19 this year. More details here.

As an advantage to all developers, blockchain enthusiasts and crypto geeks who are aching to solve this challenge, here is an interview with Steven Pu, Founder of Taraxa which defines data collection in detail and its benefits/or not, and may be a few tips that might help in cracking this challenge.

Taraxa is a fast, scalable, and device-friendly public ledger designed to help IoT ecosystems become more trusted, autonomous, and valuable with the mission to build IoT’s trust anchor. Taraxa is built from the ground up, by a team of accomplished engineers and academics headquartered in Silicon Valley, hailing from prestigious academic institutions such as Stanford, Princeton, Brown, and Berkeley, all with a passion for enabling the machine to machine (M2M) economy of the future. Edited Excerpts of the interview:

Blockchain sounds like a generic infrastructural technology, what about Taraxa? Is it IoT-specific?

A large subset of IoT applications are stateless. Data anchoring, for example, where IoT devices make periodic commitments into the blockchain about the data they’ve collected, is a stateless operation. This means each transaction is not dependent on past or future transactions. Our protocol has two layers, the top DAG layer and the bottom finalization layer. The DAG layer gives information about transaction inclusion, which means for an IoT device performing a stateless transaction, as long as the transaction is included, it is fine, since such transactions are not impacted by ordering. Our unique design allows IoT devices to obtain this information much faster and earlier than other protocols, in which inclusion, finalization and execution are all tightly bounded into a single step.

Many IoT devices are resource-constrained and cannot run full nodes. That means they cannot completely independently store the entirety of the blockchain’s history, or have the computational resources to verify transactions, or the bandwidth to synchronize with the network. These are usually called light nodes. Current light node designs rely completely on a full node to update its state, giving that specific full node the opportunity to deceive or corrupt any light node it is connected to. Taraxa has a built-in mechanism whereby a light node could query a rando subset of the network for a re-validation of what it has been told by the full node it usually communicates with, giving it the capability to remain far more independent and trustless than what current conventional designs allow.

Why is there a need for anonymized data collection?

With the rapid proliferation of digitized technologies, the public at large has become increasingly aware of the omnipresence of data-collecting sensors as well as concerned about how they’re being used. Recent scandals involving Facebook and Google’s mishandling of user data sparked concerns worldwide among the public as well as regulators. The EU’s General Data Protection Regulation (GDPR) that came into effect in May of 2018 further placed privacy and data ownership at the center of civil discourse. These regulatory trends however are still extremely limited in scope in that they mostly require a user consent upon visiting websites which only acknowledges problem without fundamentally solving it. These concerns are especially thorny in the case of IoT devices, as they have increasingly become embedded directly into our environments without our knowledge, tracking everything from location and movement to voice and video. Much of this also happens with numerous third-parties whose involvement and activities are difficult to track, as well as across political jurisdictions each with their uniquely different regulatory requirements, further complicating social concerns.

If IoT as a technology is to continue proliferation, it must address data privacy concerns head-on and provide socially-acceptable solutions to guarantee secure data ownership and usage without triggering innovation-killing regulatory backlashes.

Are there any successful machine learning applications for anonymized data collection?

Short answer is – yes, any machine learning application can run on this type of data since the data itself is in plain text.

There are two types of anonymity – the anonymization of data source, and the anonymization of the data itself. This challenge is the former, but I will talk about both.

Anonymizing the data source means exactly that – you don’t know where the data came from, but you know that it is real, valid data. In this case, it is simply raw data like anything else, and you may run any machine learning algorithms over them to build applications.

Anonymizing the data itself is much more complex. It usually is done through two methods, via software or hardware. The software method involves what’s called homomorphic encryption, which allows an algorithm to perform arbitrary operations directly on encrypted data, without knowing what that data is. Fully homomorphic encryption is incredibly slow, roughly 50,000 – 100,000 times slower than normal execution. The hardware solution involves trusted execution environment (TEE), which cordons off a section of a processor that requires specific permissions (via cryptographic signatures) to access, effectively preventing unauthorized or malicious programs from accessing restricted memory. Much of the key storage, signing & validation processes are also hard-wired into the hardware so that process is impossible to hack.

What are some of the examples of devices the manufacturer produces? Why do they have to be cryptographically-guaranteed?

Any device that generates data which may be sensitive.

A consumer example would be smart speakers that respond to your voice commands. One persistent concern is whether companies like Google or Amazon are recording all our conversations. They tell us no but it’s difficult to tell for sure, and the machines often misinterpret conversations for commands which result in large segments of these conversations being sent to central servers. While companies need to collect data in order to offer us services, that data does not need to tie directly into our personal identities. It’s OK to know that “user X just asked about ways to cure a STD”, it is not OK to know that “user X is John Smith living at 123 Main Street”. The membership proof ensures that the companies can collect the necessary data to offer the right service, while they cannot associate that data with a person or entity.

How can the end user stop even the anonymized data collection on demand?

This could be easily done if the manufactures build in such functionalities, which they will do if users become highly privacy-conscious, enough to effect regulations that require such functionalities be built in. We are already seeing this happening across major software platforms, it is only a matter of time (very brief amount of time) before hardware platforms come under the same regulatory standards.

How is the user informed about the data collection?

The device manufacturer needs to build in functionality that allows the users to monitor such data collection (see previous question).

What could also be done independently of the manufacturer is to use packet-sniffing software that analyzes real time network traffic and understand what types of data is being sent and received. These types of software are usually used by network administrators or security professionals to protect their systems.

Does this challenge interest you? Apply here for the Hackathon before the 12th of May – the event is free for all developers who apply. Also, there is more. If you are a developer or aspiring entrepreneur in the blockchain/crypto space and  want to know about the investment perspectives from Top Asian & European Funds in the Blockchain segment or business use cases in real word adoption, get your free tickets for Hash Talk which will be an afternoon-long summit focused on discussions and creating insights on investment, business, and tech in blockchain curated and brought by LongHash Germany. More details here.

Anyone who has ever had their bag go AWOL while travelling knows that sinking feeling that comes with being told that your luggage is lost. Sadly, lost luggage is one of the perils of modern travel, and one that seems to happen on an all-too-often basis. Fortunately, just as technology is influencing the way we reach our destination, it’s also having an impact on our luggage, its transfer, tracking and safe arrival.

As the number of travellers across the globe has increased incrementally over the years, so too have the amount of suitcases and bags that need to move from point A to point B. While self-check in via website, app or kiosk has become something of a staple, checking in baggage and keeping track of it isn’t always as simple, but this looks set to change.

Sophisticated technology is altering the way our bags move when we do, and streamlining everything from the check in process to the collection. Both airlines and luggage companies have been involved in working towards smoother baggage control, and there are several developments and products that are going to make the future of travel so much better.

Electronic Luggage Tags

While carry on luggage lets you skip check-in queues at airports, checking bags in still takes time. Travellers need to print out tags, affix them to their bags and drop them off at the check in counter – all time consuming activities that make the whole process that much more labour intensive.

Lufthansa and German luggage manufacturer Rimowa are hoping to simplify the process and are currently rolling out a range of suitcases that come standard with an embedded E Ink display. This E Ink display is the same size as the standard paper luggage tag and it uses Bluetooth radio to collate data from supported airline apps, or Rimowa’s own app. The app then uses its luggage check in tool to synchronise with the electronic tag and the bag can be dropped at the counter, ready to be loaded onto the relevant flight.

This electronic tag system has been trialled since March 2017, and although Lufthansa is currently the only airline to support the service, and it’s only available at Munich, Hamburg and Frankfurt airports, it’s clear that the product has major potential.

Real Time Baggage Tracking

In the USA, Delta Airlines is deploying Radio Frequency Identification, or RFID technology that’s being used to track baggage in real time. This is a first for U.S carriers and marks a historic shift for Delta, as they handle 120 million bags on an annual basis. Up until now, these bags have been managed by barcode technology that was implemented in the 1990’s.

With RFID technology, scanners utilize radio waves to capture consistent, accurate data that is stored on a chip embedded in a luggage tag. This allows for complete transparency and makes tracking simple and straightforward. Travellers can see where their bags are at all times, ensure they have made it on to the plane and have been unloaded at the other side. The Fly Delta mobile app makes it possible to track baggage at all times and to ensure it reaches its destination safely.

This technology will be in use at 344 stations around the world, and has a 99.9% success rate for correct routing and loading. Travellers are given the ability to keep tabs on their bags at all times, and this has not only reduced some of the stress of travelling, it’s also drastically reduced the number of lost bags, or bags that haven’t made it to their correct final destination, as customers can easily pick up when their baggage has gone off course.

Smart Luggage

The Internet of Things is also having a major effect on travel, and is pairing smartphones with everything from our fridges to our lights. This makes our lives easier and more fluid, and it was only a matter of time before it crossed over into travel. Smart luggage is thus the next obvious step.

If carrying luggage around is too cumbersome, or you’d prefer a hands-free experience, then the Hop or Spacecase are going to make travelling so much easier. Both of these smart bags are designed to follow their owner unaided, and are equipped with Bluetooth technology and a camera sensor. Designed by NUA Robotics, these self- carry suitcases are designed to travel automatically alongside their owner when activated by a smartphone app.

These suitcases feature a proximity detector that allows them to travel at the same pace as their owner, and also have been equipped with an anti tamper security feature that sounds an alarm if separated. These prototypes are sure to spawn many other smart luggage options, and will allow for an easier, lighter travel experience. These bags can be taken on board as hand luggage, or checked in as cargo.

The Hop was designed in 2012, but it’s only now getting the attention it deserves as there is an increasing focus on changing the way we travel and making it simpler.

Microchip Tags

A number of companies have released luggage tags that contain imbedded microchips. These microchips are similar to those used to identify pets, and can be scanned by a barcode reader.  The ReboundTAG allows airline staff to scan and see what the bag owner’s itinerary is, and view their contact details in case the bag has been mislaid or not reached its intended destination. If an airport does not have a scanner they can enter the tag number on the ReboundTAG website and find the owner this way.

SuperSmartTag works in much the same way, and also ensures that bags can easily be tracked back to their owners, or sent on to the owner’s next destinations.

Magellan’s Retriever Tags are not as sophisticated, but they are just as clever. The tag contains a capsule with instructions written in 8 different languages that tell baggage handlers to check the itinerary inside your bag and send your luggage on to the next destination, rather than returning it to the initial point of departure, as is the case with so many other lost bags.

These microchip and capsule tags are a cost effective option and start at as little as a few dollars, proving that cutting edge technology doesn’t have to be prohibitively expensive.

All of these tech innovations are set to make travel that much easier, faster and convenient. It seems that by giving travellers greater control over their baggage, as well as greater insight into its movements, lost bags may well become a thing of the past, and transferring baggage from one point to another will be as easy as boarding a plane.

Like this article? Subscribe to our weekly newsletter to never miss out!

Follow @DataconomyMedia

As the collectors of actionable input information, networked smart devices with embedded sensors, software and electronics are a key driving force behind the Internet of Things (IoT).

However, they do not generate value for organizations on their own. Powerful, fast database technologies are required to create meaningful insight from the connected devices that are generating Big Data. When used correctly, the opportunities smart devices and the IoT present across sectors can be ground-breaking and deliver a real competitive advantage.

• Opportunities in every sector

The financial services sector is a well-known example of where Big Data is already having a significant impact. The move from people-driven to algorithmic trading is just one illustration of this. But, the technology that enabled this shift holds the potential to significantly benefit a myriad of industries. First movers across numerous sectors have already woken up to the potential, and it won’t be long before smart devices and the IoT become standard practice across every area of business.

A number of industry leading healthcare firms, for example, have already begun testing embedded devices to collect health metrics like electrocardiogram (ECG) movements and blood pressure readings, to deliver proactive healthcare. And in manufacturing, major companies are not just purchasing new equipment with IoT sensors pre-installed but are retrofitting them in existing factories in order to improve operational efficiency and productivity. Electricity grid operators and utilities too are looking to make use of smart devices for measuring and communicating electricity use, to improve billing, generation and reliability.

As these cases demonstrate, this new era of connected devices gives companies more detailed operational data which in turn can be used to gain actionable insights that were previously not possible.

• Database technologies for real-time insights

In practice, the rise of the use of smart sensors and the IoT across industries means that the amount of data to be processed will increase exponentially. In the Utility sector alone, we estimate that by 2025, companies could be receiving around 900 times more data than they do today. These massive volumes of data generated by IoT devices cannot be ingested by traditional solutions, and as such are beyond their scope to process.

This is where combined in-memory and on-disk database systems become key to the successful application of the IoT. Not only do these solutions have stream processing capabilities, allowing them to analyse very large quantities of data in-flight, they can also leverage historical data for real-time analytics. Some also offer support for time series, a feature which whilst rare, is very relevant for many IoT applications, such as preventative maintenance, where large quantities of data need to be collected at regular intervals for instant analysis. Combining IoT outputs with software that can analyze real time and historical data quickly can literally transform a business.

• Upgrade path for sensor analytics in the IoT

Whilst the potential for using database technologies to make sense of the data collected by smart devices is clear, it’s worth considering how industries can leverage the opportunities whilst protecting the investments they have already made in their systems’ infrastructure.

Installing an IoT-optimized database platform is one of the best ways to simplify the upgrade path to powerful sensor IoT analytics.

It’s important to bear in mind that any new technology will need to interface seamlessly with the existing platform to have minimal impact on operations. So, as well as the creation of a historical database, there should be careful planning around how the new technology can be used to bolster existing systems.  

Whilst introducing new real-time visualization and analytic tools, the requirements of business users in the organization also need to be kept front and center to ensure they are receiving maximum value from the investment. Looking for technology that is scalable, does not require a large investment in hardware, is relatively simple to maintain, performs extremely well and is highly available will pay dividends in the long term.

With more than 100 billion IoT connected devices projected to be installed by 2025, the speed of adoption of these and related technologies is increasing rapidly. What’s more, according to ABI Research, the addressable market for analytics and other value-added services for the industrial IoT is expected to reach $120 billion by 2018. Clearly, the time to start adopting and implementing sensor technologies is now.