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K.J. Kiisa

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Master thesis (2026) - K.J. Kiisa, K. Liang, H. Chen
MEGA is a popular cloud storage provider in both commercial and consumer markets [2][1]. MEGA claims to provide secure storage, in a threat model where even the storage provider should be unable to tamper with a user’s data undetected [5]. Previous work by Backendal et. al., as well as other follow-up research works, discovered several attacks that an adversarial storage provider could perform to covertly read and write a user’s storage [7]. MEGA’s patches to the attacks solve the initial attacks that allow for the attack chain to take place, but did not solve the fundamental problems in the security architecture that enabled these attacks [6]. This work provides 5 attacks on user’s contact relationships and folder sharing, that even after the patches, allow for an adversarial storage provider to manipulate a user’s contact list, and forge data in their secure storage. ...
Bachelor thesis (2024) - K.J. Kiisa, X. Zhang, M. Weinmann
Gaussian Splatting is a successful recent method for generating novel views of a scene based on photographs taken from that scene [1]. It uses rasterization in order to render the scenes it generates, which consist of 3D Gaussians. However, modern hardware and tools are designed and optimized around rendering polygonal and texture based models [2]. This paper proposes a method of extracting both a 3D model and texture file from a Gaussian Splatting scene by using renders of that scene in Photogrammetry. It shows that this can be a viable method for generating a traditional 3D model from Gaussian Splatting scene, and can for certain cases generate a model of comparable quality while lowering the number of initial images required by up to three times. ...