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Tokenizing Virtual Identity: Blockchain & AI’s Inevitable Impact

Tokenizing Virtual Identity

Tokenizing virtual identity is the latest buzzword in the world of technology. With the rise of blockchain and AI, the process of tokenizing virtual identity has become more feasible and efficient. In a world that is increasingly dependent on digital communication and transactions, virtual identity has become an essential aspect of our lives. From social media to online banking, virtual identity is crucial for individuals and organizations alike. This article explores the inevitable impact of blockchain and AI on tokenizing virtual identity.

What is Blockchain and AI?

To understand the role of blockchain and AI in tokenizing virtual identity, we need to first understand what these technologies are. Blockchain is a decentralized and distributed digital ledger that records transactions across multiple computers, allowing secure and transparent storage of data. AI, on the other hand, refers to the simulation of human intelligence in machines that can perform tasks that typically require human cognition, such as learning, reasoning, and problem-solving.

The Benefits of Tokenizing Virtual Identity

Tokenizing virtual identity offers several benefits. Firstly, it provides a higher degree of security than traditional identity management systems, as it is based on cryptography and decentralized storage. Secondly, it offers greater control and ownership of personal data, allowing individuals to manage and monetize their identity. Thirdly, it offers greater efficiency by reducing the need for intermediaries and streamlining identity verification processes.

The Role of Blockchain in Tokenizing Identity

Blockchain plays a crucial role in tokenizing virtual identity. By providing a decentralized and secure platform for storing and managing identity data, blockchain ensures that personal data is owned and controlled by individuals, rather than centralized institutions. Blockchain also enables the creation of self-sovereign identities, where individuals have complete control over their identity data and can share it securely with trusted parties.

The Role of AI in Tokenizing Identity

AI plays a crucial role in tokenizing virtual identity by automating identity verification processes. By leveraging machine learning algorithms, AI can analyze large volumes of data and make intelligent decisions about identity verification. This can help reduce the risk of fraud and improve the efficiency of identity verification processes.

Tokenizing Virtual Identity: Use Cases

Tokenizing virtual identity has several use cases. For example, it can be used for secure and decentralized voting systems, where individuals can verify their identity and cast their vote securely and anonymously. It can also be used for secure and decentralized identity verification for financial and healthcare services, reducing the risk of identity theft and fraud.

Tokenizing Virtual Identity: Challenges

Tokenizing virtual identity also presents several challenges. One of the main challenges is interoperability, as different blockchain networks and AI systems may not be compatible with each other. Another challenge is scalability, as blockchain and AI systems may not be able to handle the volume of data required for identity verification on a large scale.

Security Concerns in Tokenizing Identity

Security is a key concern in tokenizing virtual identity. While blockchain and AI offer greater security than traditional identity management systems, they are not immune to attacks. Hackers could potentially exploit vulnerabilities in blockchain and AI systems to gain access to personal data. It is therefore crucial to implement robust security measures to protect personal data.

Privacy Issues in Tokenizing Identity

Privacy is another key concern in tokenizing virtual identity. While tokenizing virtual identity offers greater control and ownership of personal data, it also raises concerns about data privacy. It is essential to ensure that personal data is not shared without consent and that individuals have the right to access, modify, and delete their data.

Legal Implications of Tokenizing Identity

Tokenizing virtual identity also has legal implications. As personal data becomes more valuable, it is crucial to ensure that there are adequate laws and regulations in place to protect personal data. It is also essential to ensure that individuals have the right to access and control their data, and that they are not discriminated against based on their identity.

The Future of Tokenizing Virtual Identity

The future of tokenizing virtual identity looks bright. As blockchain and AI continue to evolve, we can expect to see more secure, efficient, and decentralized identity management systems. We can also expect to see more use cases for tokenizing virtual identity, from secure and anonymous voting systems to decentralized identity verification for financial and healthcare services.

Embracing Blockchain & AI for Identity Management

In conclusion, tokenizing virtual identity is an inevitable trend that will revolutionize the way we manage identity. By leveraging blockchain and AI, we can create more secure, efficient, and decentralized identity management systems that give individuals greater control and ownership of their personal data. While there are challenges and concerns associated with tokenizing virtual identity, these can be addressed through robust security measures, privacy protections, and adequate laws and regulations. As we continue to embrace blockchain and AI for identity management, we can look forward to a more secure, efficient, and decentralized future.

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Ransomware is already out of control. AI-powered ransomware could be ‘terrifying.’

Hiring AI experts to automate ransomware could be the next step for well-endowed ransomware groups that are seeking to scale up their attacks.
 

In the perpetual battle between cybercriminals and defenders, the latter have always had one largely unchallenged advantage: The use of AI and machine learning allows them to automate a lot of what they do, especially around detecting and responding to attacks. This leg-up hasn’t been nearly enough to keep ransomware at bay, but it has still been far more than what cybercriminals have ever been able to muster in terms of AI and automation.

That’s because deploying AI-powered ransomware would require AI expertise. And the ransomware gangs don’t have it. At least not yet.

But given the wealth accumulated by a number of ransomware gangs in recent years, it may not be long before attackers do bring aboard AI experts of their own, prominent cybersecurity authority Mikko Hyppönen said.

Some of these groups have so much cash — or bitcoin, rather — that they could now potentially compete with legit security firms for talent in AI and machine learning, according to Hyppönen, the chief research officer at cybersecurity firm WithSecure.

Ransomware gang Conti pulled in $182 million in ransom payments during 2021, according to blockchain data platform Chainalysis. Leaks of Conti’s chats suggest that the group may have invested some of its take in pricey “zero day” vulnerabilities and the hiring of penetration testers.

“We have already seen [ransomware groups] hire pen testers to break into networks to figure out how to deploy ransomware. The next step will be that they will start hiring ML and AI experts to automate their malware campaigns,” Hyppönen told Protocol.

“It’s not a far reach to see that they will have the capability to offer double or triple salaries to AI/ML experts in exchange for them to go to the dark side,” he said. “I do think it’s going to happen in the near future — if I would have to guess, in the next 12 to 24 months.”

If this happens, Hyppönen said, “it would be one of the biggest challenges we’re likely to face in the near future.”

AI for scaling up ransomware

While doom-and-gloom cybersecurity predictions are abundant, with two decades of experience on matters of cybercrime, Hyppönen is not just any prognosticator. He has been with his current company, which until recently was known as F-Secure, since 1991 and has been researching — and vying with — cybercriminals since the early days of the concept.

In his view, the introduction of AI and machine learning to the attacker side would be a distinct change of the game. He’s not alone in thinking so.

When it comes to ransomware, for instance, automating large portions of the process could mean an even greater acceleration in attacks, said Mark Driver, a research vice president at Gartner.

Currently, ransomware attacks are often very tailored to the individual target, making the attacks more difficult to scale, Driver said. Even still, the number of ransomware attacks doubled year-over-year in 2021, SonicWall has reported — and ransomware has been getting more successful as well. The percentage of affected organizations that agreed to pay a ransom shot up to 58% in 2021, from 34% the year before, Proofpoint has reported.

However, if attackers were able to automate ransomware using AI and machine learning, that would allow them to go after an even wider range of targets, according to Driver. That could include smaller organizations, or even individuals.

“It’s not worth their effort if it takes them hours and hours to do it manually. But if they can automate it, absolutely,” Driver said. Ultimately, “it’s terrifying.”

The prediction that AI is coming to cybercrime in a big way is not brand new, but it still has yet to manifest, Hyppönen said. Most likely, that’s because the ability to compete with deep-pocketed enterprise tech vendors to bring in the necessary talent has always been a constraint in the past.

The huge success of the ransomware gangs in 2021, predominantly Russia-affiliated groups, would appear to have changed that, according to Hyppönen. Chainalysis reports it tracked ransomware payments totaling $602 million in 2021, led by Conti’s $182 million. The ransomware group that struck the Colonial Pipeline, DarkSide, earned $82 million last year, and three other groups brought in more than $30 million in that single year, according to Chainalysis.

Hyppönen estimated that less than a dozen ransomware groups might have the capacity to invest in hiring AI talent in the next few years, primarily gangs affiliated with Russia.

‘We would definitely not miss it’

If cybercrime groups hire AI talent with some of their windfall, Hyppönen believes the first thing they’ll do is automate the most manually intensive parts of a ransomware campaign. TThe actual execution of a ransomware attack remains difficult, he said.

“How do you get it on 10,000 computers? How do you find a way inside corporate networks? How do you bypass the different safeguards? How do you keep changing the operation, dynamically, to actually make sure you’re successful?” Hyppönen said. “All of that is manual.”

Monitoring systems, changing the malware code, recompiling it and registering new domain names to avoid defenses — things it takes humans a long time to do — would all be fairly simple to do with automation. “All of this is done in an instant by machines,” Hyppönen said.

That means it should be very obvious when AI-powered automation comes to ransomware, according to Hyppönen.

“This would be such a big shift, such a big change,” he said. “We would definitely not miss it.”

But would the ransomware groups really decide to go to all this trouble? Allie Mellen, an analyst at Forrester, said she’s not as sure. Given how successful ransomware groups are already, Mellen said it’s unclear why they would bother to take this route.

“They’re having no problem with the approaches that they’re taking right now,” she said. “If it ain’t broke, don’t fix it.”

Others see a higher likelihood of AI playing a role in attacks such as ransomware. Like defenders, ransomware gangs clearly have a penchant for evolving their techniques to try to stay ahead of the other side, said Ed Bowen, managing director for the AI Center of Excellence at Deloitte.

“I’m expecting it — I expect them to be using AI to improve their ability to get at this infrastructure,” Bowen said. “I think that’s inevitable.”

Lower barrier to entry

While AI talent is in extremely short supply right now, that will start to change in coming years as a wave of people graduate from university and research programs in the field, Bowen noted.

The barriers to entry in the AI field are also going lower as tools become more accessible to users, Hyppönen said.

“Today, all security companies rely heavily on machine learning — so we know exactly how hard it is to hire experts in this field. Especially people who have expertise both in cybersecurity and in machine learning. So these are hard people to recruit,” he told Protocol. “However, it’s becoming easier to become an expert, especially if you don’t need to be a world-class expert.”

That dynamic could increase the pool of candidates for cybercrime organizations who are, simultaneously, richer and “more powerful than ever before,” Hyppönen said.

Should this future come to pass, it will have massive implications for cyber defenders, in the event that a greater volume of attacks — and attacks against a broader range of targets — will be the result.

Among other things, this would likely mean that the security industry would itself be looking to compete harder than ever for AI talent, if only to try to stay ahead of automated ransomware and other AI-powered threats.

Between attackers and defenders, “you’re always leapfrogging each other” on technical capabilities, Driver said. “It’s a war of trying to get ahead of the other side.”

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What is differential privacy in machine learning (preview)?

How differential privacy works

Differential privacy is a set of systems and practices that help keep the data of individuals safe and private. In machine learning solutions, differential privacy may be required for regulatory compliance.

Differential privacy machine learning process.

In traditional scenarios, raw data is stored in files and databases. When users analyze data, they typically use the raw data. This is a concern because it might infringe on an individual’s privacy. Differential privacy tries to deal with this problem by adding “noise” or randomness to the data so that users can’t identify any individual data points. At the least, such a system provides plausible deniability. Therefore, the privacy of individuals is preserved with limited impact on the accuracy of the data.

In differentially private systems, data is shared through requests called queries. When a user submits a query for data, operations known as privacy mechanisms add noise to the requested data. Privacy mechanisms return an approximation of the data instead of the raw data. This privacy-preserving result appears in a report. Reports consist of two parts, the actual data computed and a description of how the data was created.

Differential privacy metrics

Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.

Epsilon values are non-negative. Values below 1 provide full plausible deniability. Anything above 1 comes with a higher risk of exposure of the actual data. As you implement machine learning solutions with differential privacy, you want to data with epsilon values between 0 and 1.

Another value directly correlated to epsilon is delta. Delta is a measure of the probability that a report isn’t fully private. The higher the delta, the higher the epsilon. Because these values are correlated, epsilon is used more often.

Limit queries with a privacy budget

To ensure privacy in systems where multiple queries are allowed, differential privacy defines a rate limit. This limit is known as a privacy budget. Privacy budgets prevent data from being recreated through multiple queries. Privacy budgets are allocated an epsilon amount, typically between 1 and 3 to limit the risk of reidentification. As reports are generated, privacy budgets keep track of the epsilon value of individual reports as well as the aggregate for all reports. After a privacy budget is spent or depleted, users can no longer access data.

Reliability of data

Although the preservation of privacy should be the goal, there’s a tradeoff when it comes to usability and reliability of the data. In data analytics, accuracy can be thought of as a measure of uncertainty introduced by sampling errors. This uncertainty tends to fall within certain bounds. Accuracy from a differential privacy perspective instead measures the reliability of the data, which is affected by the uncertainty introduced by the privacy mechanisms. In short, a higher level of noise or privacy translates to data that has a lower epsilon, accuracy, and reliability.

Open-source differential privacy libraries

SmartNoise is an open-source project that contains components for building machine learning solutions with differential privacy. SmartNoise is made up of the following top-level components:

  • SmartNoise Core library
  • SmartNoise SDK library

SmartNoise Core

The core library includes the following privacy mechanisms for implementing a differentially private system:

Component Description
Analysis A graph description of arbitrary computations.
Validator A Rust library that contains a set of tools for checking and deriving the necessary conditions for an analysis to be differentially private.
Runtime The medium to execute the analysis. The reference runtime is written in Rust but runtimes can be written using any computation framework such as SQL and Spark depending on your data needs.
Bindings Language bindings and helper libraries to build analyses. Currently SmartNoise provides Python bindings.

SmartNoise SDK

The system library provides the following tools and services for working with tabular and relational data:

Component Description
Data Access

Library that intercepts and processes SQL queries and produces reports. This library is implemented in Python and supports the following ODBC and DBAPI data sources:

  • PostgreSQL
  • SQL Server
  • Spark
  • Preston
  • Pandas
Service Execution service that provides a REST endpoint to serve requests or queries against shared data sources. The service is designed to allow composition of differential privacy modules that operate on requests containing different delta and epsilon values, also known as heterogeneous requests. This reference implementation accounts for additional impact from queries on correlated data.
Evaluator

Stochastic evaluator that checks for privacy violations, accuracy, and bias. The evaluator supports the following tests:

  • Privacy Test – Determines whether a report adheres to the conditions of differential privacy.
  • Accuracy Test – Measures whether the reliability of reports falls within the upper and lower bounds given a 95% confidence level.
  • Utility Test – Determines whether the confidence bounds of a report are close enough to the data while still maximizing privacy.
  • Bias Test – Measures the distribution of reports for repeated queries to ensure they aren’t unbalanced

Next steps

Learn more about differential privacy in machine learning:

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Responsible AI – Privacy and Security Requirements

Training data and prediction requests can both contain sensitive information about people / business which has to be protected. How do you safeguard the privacy of the individuals? What steps are taken to ensure that individuals have control of their data? There are regulations in countries to ensure privacy and security.

 In Europe you have the GDPR (General Data Protection Regulations) and in California there is CCPA (California Consumer Privacy Act,). Fundamentally, both give an individual control over its Data and requires that companies should protect the Data being used in the model. When Data processing is based on consent, then am individual has the right to revoke the consent at any time.

 Defending ML Models against attacks – Ensuring privacy of consumer data:

 I have discussed about very briefly about the tools for adversarial training – CleverHans and FoolBox Python libraries here: Model Debugging: Sensitivity Analysis, Adversarial Training, Residual Analysis  . Let us now look at more stringent means of protecting a ML model against attacks. It is important to protect the ML model against attacks, thus, ensuring the privacy and security of data. An ML model may be attacked in different ways – some literature classifies the attacks into: “Information Harms” and “Behavioural Harms”. Information Harm occurs when the information is allowed to leak from the model. There are different forms of Information Harms: Membership Inference, Model Inversion and Model Extraction. In Membership Inference, the attacker can determine if some information is part of the training data or not. In Model Inversion, the attacker can extract all the training data from the model and Model Extraction, the attacker is able to extract the entire model!

 Behavioural Harm occurs when the attacker can change the behaviour of the ML model itself – example: by inserting malicious data. In this post – I have given an example of an autonomous vehicle in this article: Model Debugging: Sensitivity Analysis, Adversarial Training, Residual Analysis

Cryptography | Differential privacy to protect data

You should consider privacy enhancing technologies like Secure Multi Party Computation ,(SMPC) and Fully Homomorphic Encryption (FHE). SMPC involves multiple systems to train or serve the model whilst the actual data is kept secure

In FHE the data is encrypted. Prediction requests involve encrypted data and training of the model is also carried out on encrypted data. This results in heavy computational cost because the data is never decrypted except by the user. Users will send encrypted prediction requests and will receive back an encrypted result. The goal is that using cryptography you can protect the consumers data.

Differential Privacy in Machine Learning

Differential privacy involves protection of the data by adding noise to the data so that the attackers cannot identify the real content. SmartNoise is an open-source project that contains components for building machine learning solutions with differential privacy. SmartNoise is made of following top level components:

✔️Smart Noise Core Library

✔️Smart Noise SDK Library

This is a good read to understand about Differential Privacy: https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

 Private Aggregation of Teacher Ensembles (PATE)

This follows the Knowledge Distillation concept that I discussed here: Post 1- Knowledge DistillationPost – 2 Knowldge Distillation. PATE begins by dividing the data into “k” partitions with no overlaps. It then trains k models on that data and then aggregates the results on an aggregate teacher model. During the aggregation for the aggregate teacher, you will add noise to the data and the output.

For deployment, you will use the student model. To train the student model you take unlabelled public data and feed it to the teacher model and the result is labelled data with which the student model is trained. For deployment, you use only the student model.

The process is illustrated in the figure below:

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PATE (Private Aggregation of Teacher Ensembles)

Source

Credits: