Mobile Internet services harvest data in their big data infrastructure from multiple sources and multiple data dimensions with characteristics including scale, diversity, speed and possibly others like credibility or business value. Big data analysis drives nearly every aspect of mobile Internet services to improve service quality and user experience.
Security-related misbehaviour detection mechanism based on big data analysis for connected vehicles.
The connectivity of vehicles is increasing and the number of vulnerabilities is also increasing with the complex technology development. It makes the connected vehicles face more threats. Big data analysis improves security analysis a lot, and the huge amount of automotive data analysis is very useful for connected vehicles security. This Recommendation addresses security-related misbehaviour detection mechanism based on big data analysis for connected vehicles, which could be helpful for stakeholders to utilize the automotive data to improve vehicle security.
This recommendation is now under study.
Big data based on cloud computing provides the capabilities to collect, store, analyze, visualize and handle varieties of large volume datasets, which cannot be rapidly transferred and analyzed using traditional technologies. e.g. Big Data as a Service (as defined in [ITU-T Y.3600], big data as a service (BDaaS) is a cloud service category in which the capabilities provided to the cloud service customer are the ability to collect, store, analyse, visualize and manage data using big data.). Data storage, analysis, calculation and other data services based on the big data platform, are developing rapidly in recent years.
This recommendation aims to specify security protection measures of big data platform, regulate security protection measures in the construction and operation process of big data platform, and promote the development of big data services. These measures in the framework will take into account on the national legal and regulatory obligations in individual member states in which the big data platforms operate. The work will proceed using the methodology standardized in clause 10 of Recommendation ITU-T X.1601.
This Recommendation is security guidelines of lifecycle management for telecom Big Data. This recommendation covers as follows:
- Introduction the use cases in telecom Big Data;
- Analyze the security risks of lifecycle management for telecom Big Data;
- Specify the security guidelines of lifecycle management for telecom Big Data.
The ITU-T Focus Group on Artificial Intelligence for Health (AI4H) was established by ITU-T Study Group 16 at its meeting in Ljubljana, Slovenia, 9-20 July 2018. The Focus Group will work in partnership with the World Health Organization (WHO) to establish a standardized assessment framework for the evaluation of AI-based methods for health, diagnosis, triage or treatment decisions. Participation in the FG-AI4H is free of charge and open to all.
The FG-AI4H will pursue the following broad sets of goals:
1. To be a platform to facilitate a global dialogue for AI for health.
2. To collaborate with WHO in developing appropriate national guidance documents for establishing policy-enabled environment to ensure the safe and appropriate use of AI in health.
3. To identify standardization opportunities for a benchmarking framework that will enable broad use of AI for health.
4. To create a technical framework and standardization approach of AI for health algorithm assessment and validation.
5. To develop open benchmarks, targeted to become international standards, and serve as guidance for the assessment of new AI for health algorithms.
6. To develop, together with WHO, an assessment framework for an evaluation and validation process of AI for health.
7. To collaborate with stakeholders to monitor and collect feedback from the use of AI algorithms in healthcare delivery environment, and to provide feedback to development of improved international standards.
8. To generate a transparent documentation by creating reports and specifications towards enabling external assessment of the benchmarking framework and the benchmarked AI for health methods.
ISO/IEC 19944
- extends the existing cloud computing vocabulary and reference architecture in ISO/IEC 17788 and ISO/IEC 17789 to describe an ecosystem involving devices using cloud services,
- describes the various types of data flowing within the devices and cloud computing ecosystem,
- describes the impact of connected devices on the data that flow within the cloud computing ecosystem,
- describes flows of data between cloud services, cloud service customers and cloud service users,
- provides foundational concepts, including a data taxonomy, and
- identifies the categories of data that flow across the cloud service customer devices and cloud services.
ISO/IEC 19944 is applicable primarily to cloud service providers, cloud service customers and cloud service users, but also to any person or organization involved in legal, policy, technical or other implications of data flows between devices and cloud services.
Distributed ledger technology (DLT) refers the processes and related technologies that enable nodes in a network to securely propose, validate and record state changes (or updates) to a synchronised ledger that is distributed across the network’s nodes.
The ITU-T Focus Group on Application of Distributed Ledger Technology (FG DLT) was established in May 2017
Recommendation ITU‑T Y.3602 describes a model and operations for big data provenance. Also, this Recommendation provides the functional requirements for big data service provider (BDSP) to manage big data provenance. The reliability of data is an important factor in determining the reliability of the analysis result. Data provenance aims to ensure the reliability of data by providing transparency of the historical path of the data. In a big data environment, complex data processing and migration due to the big data lifecycle and data distribution cause various difficulties in managing data provenance.
Recommendation ITU-T F.743.7 specifies requirements for visual surveillance enhanced by big data (VSBD) services. It promotes the value of visual surveillance services by using big data analytics method and tools. Massive video, event and sensing data are analysed to support enhanced visual surveillance services, including video retrieval, event detection and status prediction.
SG20 develops international standards to enable the coordinated development of IoT technologies, including machine-to-machine communications and ubiquitous sensor networks. A central part of this study is the standardization of end-to-end architectures for IoT, and mechanisms for the interoperability of IoT applications and datasets employed by various vertically oriented industry sectors.
ITU-T SG 20 Meetings Documents are available here
Recommendation Y.3600 provides requirements, capabilities and use cases of cloud computing based big data as well as its system context. Cloud computing based big data provides the capabilities to collect, store, analyze, visualize and manage varieties of large volume datasets, which cannot be rapidly transferred and analysed using traditional technologies.
In a mobile network, a great deal of traffic data which reflects the real status of the mobile network and customers' actual experience is generated all the time. Based on the big data generated from the mobile network more efficient management and reasonable planning of mobile networks can be achieved.
