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A recent case where a maintenance worker exploited device‑management flaws to steal confidential files for foreign spies highlights the need for heightened vigilance, strict self‑discipline, and prompt reporting, offering practical steps to safeguard against similar security breaches.
Researchers from Truffle Security revealed that GitHub's delete function often fails to truly remove data, exposing a new Cross Fork Object Reference (CFOR) vulnerability that allows anyone with a commit hash to access deleted or private repository data, posing serious security risks.
The article details a security case where an unauthenticated university API allowed an attacker to enumerate and download thousands of students' personal data by manipulating pagination parameters after discovering default credentials from a leaked PDF.
This article examines recommendation system problem definitions, differences between academia and industry, offline evaluation pitfalls and data leakage issues, data construction challenges with datasets like MovieLens, and evaluates whether large language models can serve as effective solutions for modern recommendation tasks.
This article examines how large language models fit into recommendation systems by discussing problem definitions, offline evaluation pitfalls such as data leakage, dataset construction issues exemplified by MovieLens, and the practical limits of using LLMs as a universal solution.
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A CCTV investigation revealed that some mobile browsers and integrated third‑party SDKs silently read users’ clipboard, capturing bank account numbers, passwords and other personal data in plaintext even when running in the background, highlighting serious privacy risks under China’s Personal Information Protection Law.
This article examines the rapid growth of China’s digital economy, the escalating risk of data leaks, and how digital watermarking—across images, text, and databases—can be applied to trace leaks, protect privacy, and address practical challenges in e‑commerce environments.
The article explores the rapid growth of China's digital economy, the escalating risk of data leaks, and how digital watermarking—across images, text, and databases—can be employed to trace leakage sources, protect e‑commerce data, and address practical challenges in security implementations.
In today's big‑data era, excessive stress leads many to seek relief through risky online activities, but unauthorized app permissions and visits to dubious sites can expose personal information, so users must stay vigilant, limit permissions, avoid harmful sites, and use security tools to protect their mobile privacy.
Even when you use a browser’s incognito or private mode, your search queries, visited sites, and personal data can still be captured by browsers, cookies, your employer, ISP, and even input methods, making true online anonymity virtually impossible.
The article surveys typical challenges in recommendation systems, including ambiguous evaluation metrics, the trade‑off between precise algorithms and user experience, the exploration‑exploitation dilemma, and why offline AUC improvements often lead to online CTR/CPM drops due to data leakage, feature inconsistency, and distribution shifts.
In this interview, a solo female participant from Shanghai Jiao Tong University shares her experience, challenges, and technical insights—including feature engineering, memory management, and model tuning with XGBoost and LightGBM—gained while competing in the Tencent Social Ads algorithm contest.
The authors share their first‑time competition experience, detailing common pitfalls in feature engineering, data leakage, and model choice, and provide concrete suggestions for beginners and mid‑level teams to improve performance in a social advertising algorithm contest.
The article explains how various companies use technologies such as ultrasonic audio, facial recognition, smart TV monitoring, and image analysis to collect personal data, outlines the common pathways of information leakage, and offers practical advice for safeguarding online privacy.