[Weekly Signal] LGU+ CEO Hong Beom-sik Set to Appear at Comprehensive Audit on the 23rd
IT DAILY ·
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Hong Beom-sik, CEO of LG Uplus, is scheduled to appear as a witness at the Ministry of Science and ICT’s comprehensive audit on the 23rd.
The National Assembly’s Science, ICT, Broadcasting, and Communications Committee approved a request at its plenary session on the 15th for five witnesses, including Hong Beom-sik, and three reference witnesses to appear.
LG Uplus CEO Hong Beom-sik and Hong Kwan-hee, CISO, were included on the witness list, and three domestic mobile carriers were excluded from the 2027-2028 GSMA next board list.
Among this week’s telecom industry issues, attention is first turning to the schedule related to Hong Beom-sik, CEO of LG Uplus, at the National Assembly audit.
Hong Beom-sik, CEO of LG Uplus, is scheduled to appear as a witness at the Ministry of Science and ICT’s comprehensive audit on the 23rd.
The National Assembly’s Science, ICT, Broadcasting, and Communications Committee held a plenary session on the 15th.
That day, the committee approved a request for witness appearances for the comprehensive audit involving five witnesses, including Hong Beom-sik, and three reference witnesses.
Accordingly, as the committee approved the request at its plenary session on the 15th, Hong’s appearance as a witness at the Ministry of Science and ICT’s comprehensive audit on the 23rd was announced.
LG Uplus CEO Hong Beom-sik and Hong Kwan-hee, CISO, were included on the witness list. Hong Beom-sik has previously appeared before the National Assembly in connection with last year’s telecom company personal information breach incident. As background for putting LG Uplus executives and the security chief on the witness list, lawmakers cited both that appearance history and the progress of the response to the server breach.
LG Uplus was notified by KISA on July 19 last year of suspected hacking of an account management (APPM) server. LG Uplus then physically disposed of the suspected compromised server on July 31, 12 days after the notification. LG Uplus reinstalled the OS on the suspected compromised server on August 12.
Hong Kwan-hee, CISO, appeared as a witness before the National Assembly’s Science, ICT, Broadcasting, and Communications Committee audit on the 6th and answered lawmakers’ questions. Hong said that he preserved the original data when necessary, but that there were some shortcomings. He also said that if a fault on its part was recognized, the company would exempt penalties and proceed after internal discussion.
GSMA announced the composition of its board for the 2027-2028 term on the 5th. The next board term runs from January 1 next year to December 31, 2028, for 2 years.
The next board includes global telecom companies such as AT&T of the U.S., Deutsche Telekom of Germany, Vodafone of the U.K., China Mobile of China, and NTT Docomo of Japan.
KT had been a board member for the 2025-2026 term, but was excluded from the next 2027-2028 board list.
SK Telecom and LG Uplus were also excluded from the next board list. As a result, no South Korean mobile carriers will participate in GSMA’s next board.
Separately, a KAIST research team recently developed a technology to detect stress based on the analysis of repeated daily behaviors while using a mobile phone. The developed technology is called SHIRBT-MMF, and it is intended to determine daily stress status.
Instead of app usage time or the number of launches, the team focused on what purpose-driven behaviors users repeatedly perform on their smartphones. Based on behavioral examples such as "messenger chat," "web browsing," and "financial payments," it classified behaviors with the same purpose regardless of app changes. The analysis factors included the time spent on each behavior, frequency, and text input patterns, and for privacy protection, actual input text and message content were not stored. The team defined these repeated behaviors as smartphone interaction-based routine behavioral tasks (SHIRBT).
The team then used its in-house method, multi-level sequential pattern mining (ML-SPM), to extract repeated screen-transition patterns. It also used LLMs to assign easy-to-understand behavioral names to the extracted patterns, with examples including "messenger chat" and "web browsing." The team evaluated whether the same behavior could be classified consistently across different email, web browser, and messenger apps, and the evaluation accuracy came in at over 95%.
The team analyzed real-life data from 26 college students and office workers in their 20s and 30s to build a personalized model that distinguishes days with and without stress based on the self-assessments they wrote each day. Among the models it built, the average accuracy of the top-performing model, broken down into short-term and long-term participants, was 75.9% for short-term participants and 73.8% for long-term participants.
Source: IT DAILY · Seong Won-young
Original: https://www.itdaily.kr/news/articleView.html?idxno=242257
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Source: IT DAILY
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