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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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New research predicts six key trends in the consumer IoT market

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Smart home IoT company Viomi Technology and the International Data Corporation have jointly issued a white paper that identifies key consumer trends for the Internet of Things and the smart home.

With the smart home, connected services and the Internet of Things overall gaining greater acceptance it is important for businesses to understand where the technology is heading next. Focusing on the home market, smart home Internet of Things company Viomi Technology, in collaboration with the market intelligence company International Data Corporation (IDC), has issued a white paper that signals the key consumer trends that are set to shape the home IoT market over the next few years.

The new paper is called “Consumer IoT Outlook 2025“, and as the title suggests it forecasts the primary trends in the consumer IoT market from now through to 2025. These trends are:

  • Computing capabilities of consumer IoT devices will increase rapidly. For this, artificial intelligence is vital to the future development of consumer IoT. The main developments will be with sensing technology, data acquisition capability and decision-making intelligence.
  • Different network protocols will work together as a hybrid network. The aim here is to provide consumers with stable and fast connection anywhere and anytime. This will be enhanced by 5G, and increased consumer expectations for connection anywhere and anytime.
  • Edge computing and local storage will be widely used on smart devices. This move will improve computing efficiency and personal privacy.
  • Consumer IoT devices will have more open integration in terms of technology. Interoperability should be achieved by breaking the boundaries between products, platforms, and applications.
  • Human-device interaction will be more user-friendly and feel more natural. This will be seen with applications like voice-, image-, face-, and touch-based interaction.
  • Smart devices will soon move into the stage of proliferation. The main growth area, the report suggests, will probably be in China.

The research will be presented by Viomi at the Appliances & Electronics World Expo in Shanghai, China on March 13, 2019.

At the same time, a separate report from market research firm Grand View Research predicts that the global smart home automation market will hit $130 billion by 2025, compared to $46.15 billion in 2016.

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The cloud strategy that Microsoft is leading and that Google and Amazon are betting on is growing, report says

Business Insider

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Microsoft CEO, Satya Nadella. - Photo by LeWeb
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  • According to Flexera’s RightScale 2019 State of the Cloud Report, the amount of large companies with a hybrid cloud strategy, or combining public clouds and data centers to store workloads, has risen from 51% to 58%.
  • Microsoft is the leader in hybrid cloud, as it introduced its hybrid cloud Azure Stack in 2017.
  • Google Cloud and Amazon Web Services have also announced hybrid cloud offerings in the past year.

For a long time, Microsoft has been touting hybrid cloud, or a mix of on-premises and public cloud services.

And in the past year, both Amazon Web Services and Google Cloud have followed suit, making major announcements around hybrid cloud. Companies often choose to keep some of their work on data centers due to regulations, especially in industries like health or finance, and analysts say this will not change anytime soon.

Indeed, 58% of companies with more than 1,000 employees are now pursuing a hybrid cloud strategy, up from 51% last year, Flexera’s RightScale 2019 State of the Cloud Report says.

What’s more, 84% of those companies have a multi-cloud strategy, which means that they store workloads on multiple public clouds, hybrid clouds or data centers. This rose from 81% last year.

Microsoft launched its hybrid cloud Azure Stack in 2017, and currently, Microsoft is the only company out of the top three cloud providers that has a generally available hybrid cloud.

Last November, Amazon announced a hybrid cloud offering calledAWS Outposts, and it will be available later this year. And in February, Google Cloud announced that it will make its hybrid cloud offering Cloud Services Platform available as a beta for customers, a move that company officials say is a part of its strategy to attract more enterprise customers.

In addition, IBM is betting on its upcoming acquisition of Red Hat to help it become a top hybrid cloud player.

Now, 45% of enterprises see hybrid cloud or a balanced approach being using public clouds and data centers as their top priority in their cloud strategy, the survey found. In comparison, 31% of enterprises see public cloud as their biggest focus.

The Flexera RightScale survey polled 786 respondents, 58% of which were large, 1,000+ employee corporations and 42% of which were small businesses.

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

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Who will control the data from autonomous vehicles?

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Self-driving cars, like many inventions of the data-driven age, collect huge volumes of data, relating to the performance of the car and geospatial information. Who will, and who should, own this data? A new study assesses the importance.

Researchers from Dartmouth College have questioned the ownership of data in relation to autonomous vehicle technology. As self-driving cars advance, there will be a vast quantity of data amassed from navigational technologies. This leads to important questions that need to be asked about data privacy, ownership, cybersecurity and public safety. This is in the context of the mapping data being collected and analysed by the companies that manufacture the navigation technology.

One use that companies will make of the collected geospatial data is to develop and design new maps. These are produced through sophisticated and proprietary combinations of sensing and mapping technologies. These technologies feature continuous, multimodal and extensive data collection and processing.

Such maps will be able to identify the spaces within which people live and travel. While this can help to promote technological innovation, it raises privacy questions. The researchers are calling on the developers of the ‘black boxes’ that will be integral to autonomous cars to be more open as to what happens with the data and for the navigation devices themselves to have greater transparency.

According to lead researcher Professor Luis F. Alvarez León:

“Self-driving cars have the potential to transform our transportation network and society at large. This carries enormous consequences given that the data and technology are likely to fundamentally reshape the way our cities and communities operate.”

The new research paper proposes that governments should enact legislation that allows future autonomous cars users to unlock the ‘black box’ and understand what data is being used for and why. As León states: “oversight of the self-driving car industry cannot be left to the manufacturers themselves.” The paper also calls for developers to use open-source software, which will enable an understanding of what is happening with the data.

There is also a call for greater understanding of security risks and the extent that data can be taken from car navigation systems.

The discussion has been developed in a paper published in the journal Cartographic Perspectives. The research paper is titled “Counter-Mapping the Spaces of Autonomous Driving.”

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