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Personal Data of Approximately 220,000 Domestic and International Gangnam Unni Users Leaked
Healing Paper's Gangnam Unni beauty platform leaked personal data of roughly 220,000 domestic and international users via abnormal API access on September 4.
Healing Paper, operator of the beauty medical platform Gangnam Unni, announced on September 7 that personal information of approximately 220,000 domestic and international users was leaked. The incident involved abnormal access on September 4 to the integration API used to view consultation records. The company disclosed the event through a public notice.
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Shadow AI, deepfakes, and supply chain compromise are rewriting the financial sector threat playbook
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Unmasking Cloud Identities: From Behavioral Clustering to Automated Detection
Unit 42 clusters behavior of 40,000+ AWS identities from 125 cloud environments to map functional roles and enable lightweight SQL-based detection.
Palo Alto Unit 42 built an unsupervised behavioral clustering model using UMAP and HDBSCAN on AWS CloudTrail logs to map cloud identities to functional roles such as administrators, backup services, security tooling and DevOps. The study analyzed over 40,000 identities across 125 cloud environments over two months. The researchers show that heuristics extracted from the clustering map can be implemented in standard SQL, enabling role classification at scale without running a continuous ML pipeline. The methodology extends to audit logs from other cloud providers, SaaS and Kubernetes.
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Differential Privacy Meets Fixed Parameter Tractability: Algorithms and Lower Bounds
Theory paper combines differential privacy with fixed-parameter tractable encoders, improving approximation guarantees for combinatorial optimization and proving new lower bounds.
The paper studies combinatorial optimization under epsilon-differential privacy within the implicit encoder-decoder framework of Gupta et al. (SODA 2010), generalizing it to allow fixed-parameter tractable encoders. This circumvents approximation barriers inherent to polynomial-time algorithms and yields improved guarantees for fundamental combinatorial optimization problems. The authors establish the first representation-independent lower bounds: assuming a non-uniform variant of the Gap Exponential Time Hypothesis, no epsilon-DP encoder-decoder pair can achieve certain approximation guarantees with a subexponential-time decoder for sufficiently small epsilon. Representation-dependent lower bounds are also provided for larger epsilon.
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