Qubit conference-new-york-2021

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qubit-conference-new-york-2021: https://nyc.qubitconference.com/ Cybersecurity: Get ready for the unpredictable Create a sound cybersecurity strategy based on the right technology & budgetary insights, proven practices, and processes for SMEs. This virtual event will equip CxOs and cybersecurity teams with the right intel to create a sound cybersecurity strategy based on the right technology & budgetary insights, proven practices, and processes specially tailored for SMEs. Find out how to bring the smart design of cybersecurity architecture and processes, what to automate & how to properly set up internal and external ownership. The proven cybersecurity strategy fit for your environment can go a long way. Know what to do in-house, what to outsource, set up your budgets right, and get help from the right cybersecurity specialists.

Qubit conference-new-york-2021

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A major challenge that many organizations faces, is how to address data privacy regulations such as CCPA, GDPR and other emerging regulations around the world, including data residency controls as well as enable data sharing in a secure and private fashion. We will present solutions that can reduce and remove the legal, risk and compliance processes normally associated with data sharing projects by allowing organizations to collaborate across divisions, with other organizations and across jurisdictions where data cannot be relocated or shared. We will discuss secure multi-party computation where organizations want to securely share sensitive data without revealing their private inputs. We will review solutions that are driving faster time to insight by the use of different techniques for privacy-preserving computing including homomorphic encryption, k-anonymity and differential privacy. We will present best practices and how to control privacy and security throughout the data life cycle. We will also review industry standards, implementations, policy management and case studies for hybrid cloud and on-premises.

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Digital Transformation and the opportunities to use data in Analytics and Machine Learning are growing exponentially, but so too are the business and financial risks in Data Privacy. The increasing number of privacy incidents and data breaches are destroying brands and customer trust, and we will discuss how business prioritization can be benefit from a finance-based data risk assessment (FinDRA). More than 60 countries have introduced privacy laws and by 2023, 65% of the world’s population will have its personal information covered under modern privacy regulations. We will discuss use cases in financial services that are finding a balance between new technology impact, regulatory compliance, and commercial business opportunity. Several privacy-preserving and privacy-enhanced techniques can provide practical security for data in use and data sharing, but none universally cover all use cases. We will discuss what tools can we use mitigate business risks caused by security threats, data residency and privacy issues. We will discuss how technologies like pseudonymization, anonymization, tokenization, encryption, masking and privacy preservation in analytics and business intelligence are used in Analytics and Machine Learning. Organizations are increasingly concerned about data security in processing personal information in external environments, such as the cloud; and information sharing. Data is spreading across hybrid IT infrastructure on-premises and multi-cloud services and we will discuss how to enforce consistent and holistic data security and privacy policies. Increasing numbers of data security, privacy and identity access management products are in use, but they do not integrate, do not share common policies, and we will discuss use cases in financial services of different techniques to protect and manage data security and privacy.

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ISACA London Chapter webinar, Feb 16th 2021 Topic: “Protecting Data Privacy in Analytics and Machine Learning” Abstract: In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to put these technologies to work for databases and other data sources. When we think about developing AI responsibly, there’s many different activities that we need to think about. This session also discusses international standards and emerging privacy-enhanced computation techniques, secure multiparty computation, zero trust, cloud and trusted execution environments. We will discuss the “why, what, and how” of techniques for privacy preserving computing. We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud. We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.

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In the shadow of the global pandemic and the associated economic downturn, organizations are focused on cost optimization, which often leads to impulsive decisions to deprioritize compliance with all nonrevenue programs. Regulators have evolved to adapt with the notable increase in data subject complaints and are getting more serious about organizations that don’t properly protect consumer data. Marriott was hit with a $124 million fine while Equifax agreed to pay a minimum of $575 million for its breach. The US Federal Trade Commission, the US Consumer Financial Protection Bureau (CFPB), and all 50 U.S. states and territories sued over the company’s failure to take “reasonable steps” to secure its sensitive personal data. Privacy and data protection are enforced by a growing number of regulations around the world and people are actively demanding privacy protection — and legislators are reacting. More than 60 countries have introduced privacy laws in response to citizens’ cry for transparency and control. By 2023, 65% of the world’s population will have its personal information covered under modern privacy regulations, up from 10% today, according to Gartner. There is a convergence of data privacy principles, standards and regulations on a common set of fundamental principles. The opportunities to use data are growing exponentially, but so too are the business and financial risks as the number of data protection and privacy regulations grows internationally. Join this webinar to learn more about: - Trends in modern privacy regulations - The impact on organizations to protect and use sensitive data - Data privacy principles - The impact of General Data Protection Regulation (GDPR) and data transfer between US and EU - The evolving CCPA, the new PCI DSS version 4 and new international data privacy laws or regulations - Data privacy best practices, use cases and how to control sensitive personal data throughout the data life cycle

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BCS North London Branch in association with Central London Branch webinar (by GoToWebinar) Date: 2nd December 2020 Time: 18.00 to 19.30 Event title: Blockchain tokenization “What is tokenization in Blockchain?” Agenda Blockchain What is Blockchain? Use cases, trends and risks Vendors and platforms Data protection techniques and scalability Tokenization Digital business Convert a digital value into a digital token Local and central models Cloud Tokenization in Hybrid cloud

What is tokenization in blockchain - BCS London

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In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to put these technologies to work for databases and other data sources. When we think about developing AI responsibly, there’s many different activities that we need to think about. This session also discusses international standards and emerging privacy-enhanced computation techniques, secure multiparty computation, zero trust, cloud and trusted execution environments. We will discuss the “why, what, and how” of techniques for privacy preserving computing. We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud. We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.

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Tokenization on Blockchain is a steady trend. It seems that everything is being tokenized on Blockchain from paintings, diamonds and company stocks to real estate. Thus, we took an asset, tokenized it and created its digital representation that lives on Blockchain. Blockchain guarantees that the ownership information is immutable. Unfortunately, some problems need to be solved before we can successfully tokenize real-world assets on Blockchain. Main problem stems from the fact that so far, no country has a solid regulation for cryptocurrency. For example, what happens if a company that handles tokenization sells the property? They have no legal rights on the property and thus are not protected by the law. Another problem is that this system brings us back some sort of centralization. The whole idea of Blockchain and especially smart contracts is to create a trustless environment. Tokenization is a method that converts a digital value into a digital token. Tokenization can be used as a method that converts rights to an asset into a digital token. The tokenization system can be implemented local to the data that is tokenized or in a centralized model. We will discuss tokenization implementations that can provide scalability across hybrid cloud models. This session will position different data protection techniques, use cases for blockchain, and protecting blockchain.

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In this session, we will discuss a range of new emerging technologies for privacy and confidentiality in machine learning and data analytics. We will discuss how to use open source tools to put these technologies to work for databases and other data sources. When we think about developing AI responsibly, there’s many different activities that we need to think about. In this session, we will discuss technologies that help protect people, preserve privacy, and enable you to do machine learning confidentially. This session discusses industry standards and emerging privacy-enhanced computation techniques, secure multiparty computation, and trusted execution environments. We will discuss Zero Trust philosophy fundamentally changes the way we approach security since trust is a vulnerability that can be exploited particularly when working remotely and increasingly using cloud models. We will also discuss the “why, what, and how” of techniques for privacy preserving computing. We will review how different industries are taking opportunity of these privacy preserving techniques. A retail company used secure multi-party computation to be able to respect user privacy and specific regulations and allow the retailer to gain insights while protecting the organization’s IP. Secure data-sharing is used by a healthcare organization to protect the privacy of individuals and they also store and search on encrypted medical data in cloud. We will also review the benefits of secure data-sharing for financial institutions including a large bank that wanted to broaden access to its data lake without compromising data privacy but preserving the data’s analytical quality for machine learning purposes.

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