Enabot Introduces EBO Max, an AI-Powered Family Robot Designed to Think, Learn, and Care - TNGlobal
<a href="https://news.google.com/rss/articles/CBMiugFBVV95cUxOQlVkYW5OZndaNWozQUlnRkpQcmI5dmR0ZWRZVG93c01xRWNFSkxxaWttUnJVS1dSUGVLMm5NU3hqRlgzeDU1Wjh6ZHZxRkY1NjJvUFYzNmdWTy1YdkRjbHRTNzctbzBTdEFnUmppbmRXcENNSVZQUUlpOUJQc191MXFsa09WWF9PNXhWWE8ybTJnclhSVC1qdzlmRWh3blFicXNpblhKZ2wzUHJDYWRXTHdDZWlJWTlSY0E?oc=5" target="_blank">Enabot Introduces EBO Max, an AI-Powered Family Robot Designed to Think, Learn, and Care</a> <font color="#6f6f6f">TNGlobal</font>
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Local Node Differential Privacy
arXiv:2602.15802v2 Announce Type: replace Abstract: We initiate an investigation of node differential privacy for graphs in the local model of private data analysis. In our model, dubbed LNDP*, each node sees its own edge list and releases the output of a local randomizer on this input. These outputs are aggregated by an untrusted server to obtain a final output. We develop a novel algorithmic framework for this setting that allows us to accurately answer arbitrary linear queries about the input graph's degree distribution. Our framework is based on a new object, called the blurry degree distribution, which closely approximates the degree distribution and has lower sensitivity. Instead of answering queries about the degree distribution directly, our algorithms answer queries about the blur
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