Data anonymization is the process of mitigating direct and indirect privacy risks within data, such that there is a measurable way to ensure records cannot be attributed to a specific individual or entity. With an estimated 2.5 quintillion bytes of data being generated every day and an increasing reliance on data to power new applications, machine learning models and AI technologies, the importance of implementing effective anonymization techniques and removing any bottlenecks is crucial to accelerating future developments and innovations. This post is a general introduction to anonymization, and the tools and techniques for providing sufficient privacy protections, so that personally identifiable information (PII) is safe from exposure and exploitation. Data anonymization should be considered a continuous process; one that can require rapid iteration of applying various privacy engineering techniques and then measuring those privacy outcomes until a desired end state is reached. In the following sections, we'll dive deeper into our core tenets of the data anonymization process, and then walkthrough how you might apply them to a notional dataset.
Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).
Deep learning uses several layers of neurons between the network's inputs and outputs. The multiple layers can progressively extract higher-level features from the raw input. For example, in image processing, lower layers may identify edges, while higher layers may identify the concepts relevant to a human such as digits or letters or faces. Deep learning has drastically improved the performance of programs in many important subfields of artificial intelligence, including computer vision, speech recognition, image classification and others. Deep learning often uses convolutional neural networks for many or all of its layers.
Incorporating ethics and legal compliance into data-driven algorithmic systems has been attracting significant attention from the computing research community, most notably under the umbrella of fair8 and interpretable16 machine learning. While important, much of this work has been limited in scope to the "last mile" of data analysis and has disregarded both the system's design, development, and use life cycle (What are we automating and why? Is the system working as intended? Are there any unforeseen consequences post-deployment?) and the data life cycle (Where did the data come from? How long is it valid and appropriate?). In this article, we argue two points. First, the decisions we make during data collection and preparation profoundly impact the robustness, fairness, and interpretability of the systems we build. Second, our responsibility for the operation of these systems does not stop when they are deployed. To make our discussion concrete, consider the use of predictive analytics in hiring. Automated hiring systems are seeing ever broader use and are as varied as the hiring practices themselves, ranging from resume screeners that claim to identify promising applicantsa to video and voice analysis tools that facilitate the interview processb and game-based assessments that promise to surface personality traits indicative of future success.c Bogen and Rieke5 describe the hiring process from the employer's point of view as a series of decisions that forms a funnel, with stages corresponding to sourcing, screening, interviewing, and selection. The hiring funnel is an example of an automated decision system--a data-driven, algorithm-assisted process that culminates in job offers to some candidates and rejections to others. The popularity of automated hiring systems is due in no small part to our collective quest for efficiency.
FDA has released a number of documents that could help clarify its expectations for artificial intelligence, machine learning, and cybersecurity. These include Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) Action Plan, published in January 2021; Good Machine Learning Practice for Medical Device Development: Guiding Principles, published in October 2021; and the just-released draft guidance, Cybersecurity in Medical Devices: Quality System Considerations and Content of Premarket Submissions. The AI/ML action plan provides a "more tailored regulatory framework for AI/ML," explained Pavlovic. She referred to FDA's 2019 discussion paper, Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD) - Discussion Paper and Request for Feedback, which laid out a "total product lifecycle approach to AI/ML regulations with the understanding that AI/ML products can be iterated much more efficiently and quickly than a typical medical device implant product or something that isn't software based." This is "because there is an opportunity to add additional data to training sets on which the products were originally formulated," she said.
The following is the "100 most noteworthy artificial intelligence companies" compiled by the AI generation (tencentAI) (in alphabetical order by company name): Inspired by recent discoveries about the way the brain processes information, Cortical.io's Retina engine converts language into semantic fingerprints, and then compares the semantic relatedness of any two texts by comparing the degree of overlap of the fingerprints. CrowdFlower is a human intervention training platform for data science teams that helps clients generate high-quality custom training data. The CrowdFlower platform supports a range of use cases including self-driving cars, personal assistants, medical image tagging, content classification, social data analysis, CRM data improvement, product classification and search relevance, and more. Headquartered in San Francisco, CrowdFlower's clients include Fortune 500 and data-driven companies.
After the spread of the COVID-19 catastrophe, many societal and consumer behavioral changes ensued. With lockdowns put in place overnight, businesses and educational institutions were forced to continue their operations remotely. This phenomenon led to an inevitable surge in the adoption of technologies for routine tasks. As a result, the country witnessed an increased attempts and incidences of digital fraud. Since the beginning of the outbreak in March 2020, the attempts of fraudulent digital transactions rose by over 28% between March 2020 & 2021 compared to the previous year.
The power to do everything online is something of an ideal. Buying groceries, seeing your doctor via telehealth: the possibilities are endless. Especially with the shutdowns of the last 18 months, logging in for instant access to both essential and entertaining platforms has been a lifesaver. In tandem with the rise of online resources is the reality of breaches, fraud, and even identity theft. So far, 2021 has already seen leaks of personality identifiable information (PII) for millions of users through well-publicized incidents, such as Ubiquiti, Parler, Mimecast, Pixlr and more.
Last year, I coined 2021 the Year of Digitalism as I foresaw the increase of corporate and governmental data surveillance. Unfortunately, it is safe to say that this has come true with Big Tech becoming more powerful than ever before and governments worldwide implementing Covid tracking apps. What also happened is that the Pandemic has been a strong catalyst for digital transformation in any sector and that the world is currently changing at lightning speed. There are economic changes such as increasing inflation rates, environmental disasters caused by climate change, social changes such as The Great Resignation, and a convergence of technologies that drives technological changes. Although the world has never changed so fast as in 2021, this year was also the most stable of all the years to come in this decade.
To weather the COVID-19 storm and navigate the rapidly changing digital world, businesses are adopting artificial intelligence at a faster pace. To help our clients sail through digital transformation and create artificial intelligence solutions that deliver on their promise, we took up the challenge to identify AI trends and innovations that will define 2022. Here's what we have found. "Launching pilots is deceptively easy but deploying them into production is notoriously challenging… Although the potential for success is enormous, delivering business impact from AI initiatives takes much longer than anticipated." Some of the common reasons for AI projects' unclear financial outcomes include: Here at ITRex, we believe that one of the biggest artificial intelligence trends for 2022 (and the years after!) will be taking an incremental, ROI-driven approach to AI development.