Tootfinder

Hoy en Xixón, apoyando a las seis de La Suiza. Otro capítulo en el golpe de estado del “quien pueda hacer que haga”.
https://www.sindicatodeestudiantes.net/index.php/no…

Hello!
I have a happy favour to ask.
Last year, I had a US intern working with us and she now is looking for help that you- good reader of this account- could give.
(She needs academic survey participants)
Check out her flyer below, click the link, and please RT
http…

Research Study on Data Privacy and Collective Redress
A researcher at University College Dublin wants to conduct anonymous interviews to learn about why you would or would not participate in a legal action to rectify a data protection violation. This information can be used to help nonprofit organizations better represent you when organizations violate your data protection rights. Provide your email to receive more information. All personally identifiable information will be deleted following completion of the study.

Patient-Centred Explainability in IVF Outcome Prediction
Adarsa Sivaprasad, Ehud Reiter, David McLernon, Nava Tintarev, Siladitya Bhattacharya, Nir Oren
https://arxiv.org/abs/2506.18760

Patient-Centred Explainability in IVF Outcome Prediction
This paper evaluates the user interface of an in vitro fertility (IVF) outcome prediction tool, focussing on its understandability for patients or potential patients. We analyse four years of anonymous patient feedback, followed by a user survey and interviews to quantify trust and understandability. Results highlight a lay user's need for prediction model \emph{explainability} beyond the model feature space. We identify user concerns about data shifts and model exclusions that impact trust. Th…

$(\ell,\delta)$-Diversity: Linkage-Robustness via a Composition Theorem
V. Arvind Rameshwar, Anshoo Tandon
https://arxiv.org/abs/2506.18405 https://…

$(\ell,δ)$-Diversity: Linkage-Robustness via a Composition Theorem
In this paper, we consider the problem of degradation of anonymity upon linkages of anonymized datasets. We work in the setting where an adversary links together $t\geq 2$ anonymized datasets in which a user of interest participates, based on the user's known quasi-identifiers, which motivates the use of $\ell$-diversity as the notion of dataset anonymity. We first argue that in the worst case, such linkage attacks can reveal the exact sensitive attribute of the user, even when each dataset res…

Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Wanli Peng, Xin Chen, Hang Fu, XinYu He, Xue Yiming, Juan Wen
https://arxiv.org/abs/2506.19889

Retrieval-Confused Generation is a Good Defender for Privacy Violation Attack of Large Language Models
Recent advances in large language models (LLMs) have made a profound impact on our society and also raised new security concerns. Particularly, due to the remarkable inference ability of LLMs, the privacy violation attack (PVA), revealed by Staab et al., introduces serious personal privacy issues. Existing defense methods mainly leverage LLMs to anonymize the input query, which requires costly inference time and cannot gain satisfactory defense performance. Moreover, directly rejecting the PVA …

Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network
Yanjun Dai, Haoyang Feng, Yuan Gao
https://arxiv.org/abs/2506.13787

Analysis of Anonymous User Interaction Relationships and Prediction of Advertising Feedback Based on Graph Neural Network
While online advertising is highly dependent on implicit interaction networks of anonymous users for engagement inference, and for the selection and optimization of delivery strategies, existing graph models seldom can capture the multi-scale temporal, semantic and higher-order dependency features of these interaction networks, thus it's hard to describe the complicated patterns of the anonymous behavior. In this paper, we propose Decoupled Temporal-Hierarchical Graph Neural Network (DTH-GNN), …

Theo Francken "entend bien prouver que son parti reste suffisamment sale, pour reprendre les mots utilisés Š l’époque par Gerolf Annemans au sujet du Vlaams Belang."
#DaarDaar

"Despite good-faith efforts by DSI [dense street imagery] providers to protect individual privacy through blurring faces and license plates, these measures fail to address broader privacy concerns. In this work, we find that increased data density and advancements in artificial intelligence enable harmful group membership inferences from supposedly anonymized data."
via @…

Privacy of Groups in Dense Street Imagery
Spatially and temporally dense street imagery (DSI) datasets have grown unbounded. In 2024, individual companies possessed around 3 trillion unique images of public streets. DSI data streams are only set to grow as companies like Lyft and Waymo use DSI to train autonomous vehicle algorithms and analyze collisions. Academic researchers leverage DSI to explore novel approaches to urban analysis. Despite good-faith efforts by DSI providers to protect individual privacy through blurring faces and l…

Governments Should Mandate Tiered Anonymity on Social-Media Platforms to Counter Deepfakes and LLM-Driven Mass Misinformation
David Khachaturov, Roxanne Schnyder, Robert Mullins
https://arxiv.org/abs/2506.12814

Governments Should Mandate Tiered Anonymity on Social-Media Platforms to Counter Deepfakes and LLM-Driven Mass Misinformation
This position paper argues that governments should mandate a three-tier anonymity framework on social-media platforms as a reactionary measure prompted by the ease-of-production of deepfakes and large-language-model-driven misinformation. The tiers are determined by a given user's $\textit{reach score}$: Tier 1 permits full pseudonymity for smaller accounts, preserving everyday privacy; Tier 2 requires private legal-identity linkage for accounts with some influence, reinstating real-world accou…

Breaking Anonymity at Scale: Re-identifying the Trajectories of 100K Real Users in Japan
Abhishek Kumar Mishra, Mathieu Cunche, Heber H. Arcolezi
https://arxiv.org/abs/2506.05611 …

Breaking Anonymity at Scale: Re-identifying the Trajectories of 100K Real Users in Japan
Mobility traces represent a critical class of personal data, often subjected to privacy-preserving transformations before public release. In this study, we analyze the anonymized Yjmob100k dataset, which captures the trajectories of 100,000 users in Japan, and demonstrate how existing anonymization techniques fail to protect their sensitive attributes. We leverage population density patterns, structural correlations, and temporal activity profiles to re-identify the dataset's real-world locatio…