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Accelerating the future of privacy through SmartData agents

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Imagine a future where you can communicate with your smartphone – or whatever digital extension of you exists at that time – through an evolved smart digital agent that readily understands you, your needs, and exists on your behalf to procure the things and experiences you want. What if it could do all this while protecting and securing your personal information, putting you firmly in control of your data?

Dr. George Tomko, University of Toronto

Dr. George Tomko, University of Toronto

Dr. George Tomko Ph.D, Expert-in-Residence at IPSI (Privacy, Security and Identity Institute) at the University of Toronto, Adjunct Professor in Computer Science at Ryerson University, and Neuroscientist, believes the time is ripe to address the privacy and ethical challenges we face today, and to put into place a system that will work for individuals, while delivering effective business performance and minimizing harms to society at large. I had the privilege of meeting George to discuss his brainchild, SmartData: the development of intelligent agents and the solution to data protection.

As AI explodes, we are witnessing incident after incident from technology mishaps to data breaches, to data misuse, and erroneous and even deadline outcomes. My recent post, Artificial Intelligence needs to Reset advances the need to take a step back, slow down the course of AI, and examine these events with a view to educate, fix, prevent and regulate towards effective and sustainable implementations.

Dr. Tomko is not new to the topic of privacy. He also invented Biometric Encryption as well as the Anonymous Database in the early 90’s.  His invention of SmartData was published SmartData: Privacy Meets Evolutionary Robotics, co-authored with Dr. Ann Cavoukian, former 3-term Privacy Commissioner in Ontario and inventor of Privacy by Design. This led to his current work, Smart Data Intelligent Agents, the subject of this article.

There is an inherent danger with the current model today. How the internet evolved was not its intended path. Tim Berners-Lee envisioned an open internet, owned by no one,

…an open platform that allows anyone to share information, access opportunities and collaborate across geographical boundaries…

This has been challenged by the spread of misinformation and propaganda online has exploded partly because of the way the advertising systems of large digital platforms such as Google or Facebook have been designed to hold people’s attention…

People are being distorted by very finely trained AIs that figure out how to distract them.

What has evolved is a system that’s failing. Tomko points to major corporations and digital gatekeepers who are accumulating the bulk of the world’s personal data:

He who has the personal data has the power, and as you accumulate more personal information (personally identifiable information, location, purchases, web surfing, social media), in effect you make it more difficult for competitors to get into the game. The current oligopoly of Facebook, Google, Amazon etc will make it more difficult for companies like Duck Duck Go and Akasha to thrive.

That would be okay if these companies were to utilize the data in accordance with the positive consent of the data subject for the primary purpose intended and protected it against data hacking. However, we know that’s not happening. Instead, they are using it for purposes not intended, selling the data to third parties, transferring it to government for surveillance, often without a warrant for probable cause.

Tomko asserts if Elon Musk and the late Stephen Hawking are correct about the potential of a dystopian-like AI popularized by Skynet in The Terminator series, this is likely if the AI has access to large amounts of personal data centralized into databases. While this implies an AI with malevolent intentions, humans are relentlessly innovative and Tomko argues for the importance of putting roadblocks in place before this happens.

Enter SmartData. This is the evolution of Privacy by Design, which shifts control from the organization and places it directly in the hands of the individual (the data subject).

SmartData empowers personal data by, in effect, wrapping it in a cloak of intelligence such that it now becomes the individual’s virtual proxy in cyberspace. No longer will personal data be shared or stored in the cloud as merely data, encrypted or otherwise; it will now be stored and shared as a constituent of the binary string specifying the neural weights of the entire SmartData agent. This agent proactively builds-in privacy, security and user preferences, right from the outset, not as an afterthought.

For SmartData to succeed, it requires a radical, new approach – with an effective separation from the centralized models which exist today.

Privacy Requires Decentralization and Distribution

Our current systems and policies present hurdles we need to overcome as privacy becomes the norm. The advent of Europe’s GDPR is already making waves and challenging business today. Through GDPR’s Article 20 (The Right to Data Portability) and Article 17 (The Right to Be Forgotten), the mechanisms to download personal data, plus the absolute deletion of data belie current directives and processes. Most systems ensure data redundancy, therefore data will always exist. Systems will need to evolve to fully comply with these GDPR mandates. In addition, customer transactions on private sites are collected, analyzed, shared and sometimes sold with a prevailing mindset that data ownership is at the organizational level.

Tomko explains the SmartData solution must be developed in an open source environment.

A company that says: “Trust me that the smart agent or app we developed has no “back-door” to leak or surreptitiously share your information,” just won’t cut it any longer. Open source enables hackers to verify this information. I believe that such a platform technology will result in an ecosystem that will grow, as long as there is a demand for privacy.

Within this environment, a data utility within the SmartData platform can request all personal data under GDPR-like regulations from the organizational database. As per the SmartData Security Structure, each subject’s personal data is then cleaned and collated into content categories e.g. A = MRI data, B = subscriber data. They will be de-identified, segmented, encrypted and placed in these locked boxes (files in the cloud) identified by categorized metatags. A “Trusted Enclave” like Intel’s SGX will be associated with each data subject’s personal data. The enclave will generate a public/private key pair and output the public key to encrypt the personal data by category.

Today, information is stored and accessed by location. If breaches occur, this practice increases the risk of exposure as information about data subjects are bundled together. By categorizing and storing personal information by content, this effectively prevents personal identity to be connected with the data itself. Only SmartData will know its data subjects and pointers to their unique personal information, accessed by a unique private key.

SmartData Security Structure, George Tomko

SmartData Security Structure, George Tomko

Ensuring Effective Performance while Maintaining Individual Privacy

Organizations who want to effectively utilize data to improve efficiencies and organizational performance will take a different route to achieve this. How do companies analyze and target effectively without exposing personal data? Tomko declares that using Federated Learning, to distribute data analytics such as Machine Learning(ML) is key:

Federated Learning provides an alternative to centralizing a set of data to train a machine learning algorithm, by leaving the training data at their source. For example, a machine learning algorithm can be downloaded to the myriad of smartphones, leveraging the smartphone data as training subsets. The different devices can now contribute to the knowledge and send back the trained parameters to the organization to aggregate.  We can also substitute smartphones with the secure enclaves that protect each data subject’s personal information.

Here’s how it would work: An organization wants to develop a particular application based on machine learning, which requires some category of personal data from a large number of data-subjects as a training set. Once it has received consent from the data subjects, it would download the learning algorithm to each subject’s trusted enclave. The relevant category of encrypted personal data would then be inputted, decrypted by the enclave’s secret key, and used as input to the machine learning algorithm. The trained learning weights from all data-subjects’ enclaves would then be sent to a master enclave within this network to aggregate the weights. This iteration would continue until the accuracies are optimized. Once the algorithm is optimized, the weights would then be sent to the organization. Tomko affirms,

 

The organization will only have the aggregated weights that had been optimized based on the personal data of many data subjects. They would not be able to reverse engineer and determine the personal data of any single data subject. The organization would never have access to anyone’s personal data, plaintext or otherwise, however, would be able to accomplish their data analytic objectives.

Federated Learning - Master Enclave, George Tomko

Federated Learning – Master Enclave, George Tomko

Building a Secure Personal Footprint in the Cloud

To ensure personal web transactions are secure, a person will instruct his SmartData agent to, for example, book a flight. The instruction is transmitted to the cloud using a secure protocol such as IPSec. This digital specification (a binary string) is decrypted and downloaded to one of many reconfigurable computers, which will interpret the instructions.

Natural language (NLP) would convert the verbal instructions into formal language, as well as the encoded communications, back and forth between subject and organization to facilitate the transaction, eliciting permission for passport and payment information. What’s different is the development of an agreement (stored on the Blockchain) that confirms consented terms of use between the parties. It also adds an incentive component through cryptocurrency that enables the data subject to be compensated for their information, if required. This mechanism would be used before every transaction to ensure transparency and expediency between parties.

Tomko realizes Blockchain has its limitations:

Everyone wants to remove the intermediary and the crypto environment is moving quickly. However, we can’t rely on Blockchain alone for privacy because it is transparent, and we can’t use it for computation because it is not scalable.

AI as it exists today is going through some stumbling blocks. Most experiments are largely within ANI: Artificial Narrow Intelligence, with models and solutions built for very specific domains, which cannot be transferred to adjacent domains. Deep Learning has its limitations. The goal of SmartData is to develop a smart digital personal assistant to serve as a proxy for the data-subject across varied transactions and contexts. Tomko illustrates,

With current Deep Learning techniques, different requests such as ‘Hey SmartData, buy me a copy of …” or “book me a flight to…” encompass different domains, and accordingly, require large sets of training data specific to that domain. The different domain-specific algorithms would then need to be strung together into an integrated whole, which, in effect, would become SmartData. This method would be lengthy, computationally costly and ultimately not very effective.

The promise of AI: to explain and understand the world around us and it has yet to reveal itself.

Tomko explains:

To date, standard Machine Learning (ML) cannot achieve incremental learning that is necessary for intelligent machines and lacks the ability to store learned concepts or skills in long-term memory and use them to compose and learn more sophisticated concepts or behaviors. To emulate the human brain to explain and generally model the world, it cannot be solely engineered. It has to be evolved within a framework of Thermodynamics, Dynamical Systems Theory and Embodied Cognition.

Embodied Cognition is a field of research that “emphasizes the formative role that both the agents’ body and the environment will play in the development of cognitive processes.” Put simply, these processes will be developed when these tightly coupled systems emerge from the real-time, goal-directed interactions between the agents and their environments, and in SmartData’s case, a virtual environment. Tomko notes the underlying foundation of intelligence (including language) is action.

Actions cannot be learned in the traditional ML way but must be evolved through embodied agents. The outcomes of these actions will determine whether the agent can satisfy the data subject’s needs.

Tomko references W. Ross Ashby, a cybernetics guru from the 50’s, who proposed that every agent has a set of essential variables which serve as its benchmark needs, and by which all of its perceptions and actions are measured against. The existential goal is to always satisfy its needs. By using this model (see below), we can train the agent to satisfy the data subject’s needs, and retain the subject’s code of ethics. Essential variables are identified that determine the threshold for low surprise or high surprise. Ideally, the agent should try to maintain a low-surprise and homeostatic state (within the manifold) to be satisfied. Anything outside the manifold, i.e., high surprise should be avoided. Tomko uses Ashby’s example of a mouse, who wants to survive. If a cat is introduced, a causal model of needs is built such that the mouse uses its sensory inputs compared to its benchmark needs to determine how it will act when a cat is present and maintain its life-giving states.

Apply this to individual privacy. As per Tomko,

The survival range will include parameters for privacy protection. Therefore, if the needs change or there is a modified environment or changing context the agent will modify its behavior automatically and adapt because its needs are the puppet-master.

This can be defined as a reward function. We reward actions that result in low surprise or low entropy. For data privacy, ideally, we want to avoid any potential actions that would lead to privacy violations equating to high surprise (and greater disorder).

 

Manifold of Needs, George Tomko

Manifold of Needs, George Tomko

Toronto’s Sidewalk Labs: The Need for Alternative Data Practices

At the time of writing this article, Dr. Ann Cavoukian, Expert-in-Residence at Ryerson University, former 3-term Privacy Commissioner, resigned as an advisor to Sidewalk Labs, in Toronto, a significant project powered by Alphabet, which aimed to develop one of the first smart cities of privacy in the world. Cavoukian’s resignation resulted in a media coup nationally because of her strong advocacy for individual privacy. She explains,

My reason for resigning from Sidewalk Labs is only the tip of the iceberg of a much greater issue in our digitally oriented society.  The escalation of personally identified information being housed in central databases, controlled by a few dominant players, with the potential of being hacked and used for unintended secondary uses, is a persistent threat to our continued functioning as a free and open society.

Organizations in possession of the most personal information about users tend to be the most powerful. Google, Facebook and Amazon are but a few examples in the private sector… As a result, our privacy is being infringed upon, our freedom of expression diminished, and our collective knowledge base outsourced to a few organizations who are, in effect,  involved in surveillance fascism. In this context, these organizations may be viewed as bad actors; accordingly, we must provide individuals with a viable alternative…

The alternative to centralization of personal data storage and computation is decentralization – place all personal data in the hands of the data-subject to whom it relates, ensure that it is encrypted, and create a system where computations may be performed on the encrypted data, in a distributed manner… This is the direction that we must take, and there are now examples of small startups using the blockchain as a backbone infrastructure, taking that direction.  SmartData, Enigma, Oasis Labs, and Tim Berners-Lee’s Solid platform are all developing methods to, among other things, store personal information in a decentralized manner.

Other supporters of Dr. George Tomko concur:

Dr. Don Borrett, a practicing neurologist with a background in evolutionary robotics, with a Masters from the Institute for the History and Philosophy of Science and Technology for the University of Toronto states:

By putting control of personal data back into the hands of the individual, the SmartData initiative provides a framework by which respect for the individual and responsibility for the collective good can be both accommodated.

Bruce Pardy is a Law Professor at Queen’s University, who has written on a wide range of legal topics: human rights, climate change policy, free markets, and economic liberty, among others and he declares:

The SmartData concept is not just another appeal for companies to do better to protect personal information. Instead, it proposes to transform the privacy landscape. SmartData technology promises to give individuals the bargaining power to set their own terms for the use of their data and thereby to unleash genuine market forces that compel data-collecting companies to compete to meet customer expectations.

Dr. Tomko is correct! The time is indeed ripe, and SideWalk Labs, an important experiment that will vault us into the future, is an example of the journey many companies must take to propel us into an inevitability where privacy is commonplace.

This originally appeared om Forbes.

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Robot delivery: Bots will be bringing parcels to your home

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Ford, FedEx and Amazon are each at an advanced stage with autonomous robot delivery vehicles, designed to bring packages to the doors of businesses and homes. Several successful pilots have been completed.

Each robot looks different but the objective is similar — getting a package to a customer using an autonomous machine. The aim of these new robot delivery tools is to boost efficiency and eliminate the need to pay people to carry out the final part of the delivery process.

Ford / Agility Robotics

Ford, more commonly associated with cars and trucks, is partnering with legged locomotion specialist Agility Robotics to assess how self-driving car deliveries can be improved. The project objective is to ensure self-driving vehicles can accomplish something that’s been very difficult to accomplish: carrying out the last step of the delivery, from the car to the recipient’s front door.

The two companies hope the answer is a two-legged robot called “Digit”.

Digit has been designed to approximate the look and walk of a human. The robot is constructed from lightweight material and it is capable of lifting packages that weigh up to 40 pounds. In tests, Digit has been shown to be capable of going up and down stairs and to negotiate uneven terrain, thanks to the use of LiDAR and stereo cameras.

FedEx

The courier delivery services company FedEx is developing an autonomous delivery robot designed to assist retailers make same-day and last-mile deliveries to their customers. The device is called the FedEx SameDay Bot, and the aim is to deliver packages by bot directly to customers’ homes or businesses the same day. The device has been developed in collaboration with DEKA Development & Research Corp., run by Dean Kamen, the inventor of the Segway.

The FedEx device is the most adventurous of the three, in that it will cross roads and is destined to cover longer distances. The interaction with roads is supported by machine-learning algorithms to help the robot to detect and avoid obstacles, plot a safe path, and to follow road and safety rules.

Amazon Scout

Amazon’s autonomous delivery robots are about to begin rolling out on California sidewalks. Amazon Scout will begin with delivering packages to the company’s Prime customers residing in Southern California. The new Amazon device will work during daylight hours, providing small and medium-sized packages to customers. The Amazon Scout is a six-wheeled electric-powered vehicle around the size of a small cooler. In terms of movement, the Scout rolls along sidewalks at what’s described as a walking pace.

Amazon began testing out the Scout in January 2019, running a pilot program using six machines to deliver packages in Snohomish County, Washington. Vice president of Amazon Scout Sean Scott said: “We developed Amazon Scout at our research and development lab in Seattle, ensuring the devices can safely and efficiently navigate around pets, pedestrians and anything else in their path.”

Following the success of the pilot — where the Scout autonomously navigated the various obstacles commonly found in residential neighborhoods like trashcans, skateboards, lawn chairs, the occasional snow blower and more — the device is ready for a wider launch.

The wider launch will feature a small number of Amazon Scout devices, delivering Monday through Friday, during daylight hours in the Irvine area of California, according to Smart2Zero. Customers will order items as they would normally, but in some cases their Amazon packages will be delivered by an Amazon Scout. To make sure things go smoothly, each Scout will initially be accompanied by a human “Amazon Scout Ambassador.”

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Amazon adds fear detection and age ranges to its facial-recognition tech as the Border Patrol looks to award a $950 million contract

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  • Amazon Web Services has added several new features to its facial-recognition technology, Rekognition.
  • This includes expanded age-recognition capabilities and the new ability to recognize fear.
  • Rekognition is a controversial technology and has been the subject of much criticism and protests — from both inside and outside Amazon.
  • These new features drew some flack from commenters on Twitter.
  • Meanwhile, the US Customers and Border Patrol is looking for quotes on a sweeping new border protection system that includes more facial-recognition tech.

Amazon Web Services has expanded the capabilities of its controversial facial-recognition technology called Rekognition.

It now better detects more age ranges and it can also detect fear, the company announced in a blog post on Monday.

The company explained (emphasis ours):

“Today, we are launching accuracy and functionality improvements to our face analysis features. Face analysis generates metadata about detected faces in the form of gender, age range, emotions, attributes such as ‘Smile’, face pose, face image quality and face landmarks. With this release, we have further improved the accuracy of gender identification. In addition, we have improved accuracy for emotion detection (for all 7 emotions: ‘Happy’, ‘Sad’, ‘Angry’, ‘Surprised’, ‘Disgusted’, ‘Calm’ and ‘Confused’) and added a new emotion: ‘Fear’.Lastly, we have improved age range estimation accuracy; you also get narrower age ranges across most age groups.”

Earlier this month AWS also announced that Rekognition can now detect violent content such as blood, wounds, weapons, self-injury, corpses, as well as sexually explicit content.

But it was the news of more age ranges and fear detection that was met with comments on Twitter.

Just last month several protesters interrupted Amazon AWS CTO Werner Vogels during a keynote speech at an AWS conference in New York.

They were protesting AWS’s work with the U.S. Immigration and Customs Enforcement (ICE) and the family separation policy at the Southern Border. Amazon hasn’t acknowledged whether ICE uses its Rekognition technology, but the company did meet with ICE officials to pitch its facial-recognition tech, among other AWS services, as revealed by emails between Amazon and various government officials obtained by the American Civil Liberties Union Foundations.

Amazon’s Rekognition has come under fire from a wide range of groups who want the company to stop selling it to law enforcement agencies. In April, AI experts penned an open letter to Amazon about it. Civil rights group have protested it. 100 Amazon employees sent a letter to management last year asking the company to stop selling Rekognition to law enforcement. Another 500 signed a letter this year asking Amazon to stop working with ICE altogether.

“AWS comes under fire for Rekognition sales to the federal government, who in turn is building concentration camps for children, and AWS’s response is to improve ‘age range estimation’ and ‘fear detection’ in the service? Are you f– KIDDING ME?!” tweeted Corey Quinn from the Duckbill Group, a consultant that helps companies manage their AWS bill. Quinn also hosts theScreaming in the Cloud podcast.

Another developer tweeted, “In 25 years we’re going to be talking about how AWS handled this situation in the same way we talk about how IBM enabled the holocaust. Every engineer and ML researcher who worked on this should be ashamed of themselves.”

The CBP is looking to buy more facial-recognition tech

Meanwhile, the U.S. Customs and Border Protection (CBP), a sister agency to ICE, has put out a new request for quotes on a sweeping new border-security system that includes expanded use of facial-recognition technology.

“Integration of facial recognition technologies is intended throughout all passenger applications,” the RFQ documents say.

The CBP already uses facial recognition at various airports, such as in Mexico City, where it matches passenger’s faces with photos taken from their passports or other government documents, it says.

And the CBP uses other biometric information, such as taking fingerprints of people at the border if it suspects that they are entering the country illegally, it says.

“CBP’s future vision for biometric exit is to build the technology nationwide using cloud computing,” the agency wrote in a 2017 article about the use of facial recognition and finger-print tech.

This new contract for new border security technologies is expected to begin in early 2020 and could be worth $950 million over its lifespan, according to the RFQ documents.

This article was originally published on Business Insider. Copyright 2019.

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IBM launches ‘Trust Your Supplier’ blockchain initiative

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IBM and Chainyard have announced a new blockchain network called Trust Your Supplier, which is a blockchain-based platform that simplifies supply chain management and improves supplier qualification, validation, onboarding and life cycle information management.

IBM sees the new blockchain-based network as critical to the continued growth and advancement of the global supply chain industry. The technology provides a digital passport for supplier identity on the blockchain. This will enable suppliers to share information with any permissioned buyer on the network to make qualifying, validating and managing new suppliers easier and less time-consuming.

The Trust Your Supplier platform is being pioneered by several leading companies, such as Anheuser-Busch InBev, GlaxoSmithKline, Lenovo, Nokia, Schneider Electric and Vodafone. Each of these founding participants is in the process of onboarding their suppliers. These are leading companies across industries like technology, telecommunications, pharmaceuticals and food and beverage.

By eliminating manual, time-consuming processes, the Trust Your Supplier technology aims to help reduce the risk of fraud and errors by establishing a connected environment among global suppliers. With more than 18,500 global suppliers, IBM itself will begin using and onboarding 4,000 of its North American suppliers to the Trust Your Supplier network. This is expected to be completed during quarter 3 of 2019.

Convening a network of leading companies with shared challenges and goals, Trust Your Supplier has been designed to assist companies working across multiple industries to design and implement more efficient processes to solve a common problem in relation to the supply chain.

Representing one of the first companies to take up the service, Sanjay Mehta, Vice President Procurement, Nokia, states: “Working with IBM and Chainyard on this blockchain initiative represents a great opportunity for Nokia to further enhance our suppliers’ experience and optimize the onboarding process (process of integrating a new supplier into an organization’s network). Using the latest technology to address a classical challenge will be of benefit for everyone, and further increase the speed of using innovative solutions.”

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