<caption><image_0>The image is a presentation slide with a title that reads "NOMOATS: Towards Automatic Detection of Mobile Tracking". Below the title, there is a photo of a woman with long dark hair and a white shirt. She is smiling and looking directly at the camera. On the left side of the slide, there are two logos - one is a red circle with a white line through it, representing the logo of Broadcom Inc. and the other is a blue circle with the letters "UC Irvine". On the right side, the text "Anastasia Shuba and Abhina Markopoulos" is written in black. The slide appears to be a presentation on the topic of automatic detection of mobile tracking.</caption><transcript><audio_1>hello my name is Anastasia Shuba and I'll be presenting no more 80s towards automatic detection of mobile tracking and this is joint work with my former advisor Athena Merkel Pulu from the University of california-irvine which is where I was when this work was</transcript><transcript><audio_2>conducted currently I am at Broadcom however this presentation does not represent broad comes use so all of us here know that mobile devices have access to a wealth of sensitive data for instance our entire contact list various</transcript><transcript><audio_3>sensor information from GPS microphone and accelerometer and various device identifier for the rest of the talk I will refer to this data as PII or personally identifiable information now we know that applications often have</transcript><transcript><audio_4>legitimate reasons to collect pee for example Google Maps requires our location to function so our phone will often send our latitude and longitude coordinates to Google Maps in this case the access is legitimate because we have given Google Maps permission to collect</transcript><transcript><audio_5>our data however permissions are not enough let's take another app for example say Pokemon go it also supposedly has legitimate reasons to collect our location data and it also integrates with several third-party</transcript><transcript><audio_6>libraries for example unity3d for graphics rendering Facebook for providing like and share buttons and advertising libraries and once you've given location permissions to Pokemon go all the libraries contained within it</transcript> hello my name is Anastasia Shuba and I'll be presenting no more 80s towards automatic detection of mobile tracking and this is joint work with my former advisor Athena Merkel Pulu from the University of california-irvine which is where I was when this work was conducted currently I am at Broadcom however this presentation does not represent broad comes use so all of us here know that mobile devices have access to a wealth of sensitive data for instance our entire contact list various sensor information from GPS microphone and accelerometer and various device identifier for the rest of the talk I will refer to this data as PII or personally identifiable information now we know that applications often have legitimate reasons to collect pee for example Google Maps requires our location to function so our phone will often send our latitude and longitude coordinates to Google Maps in this case the access is legitimate because we have given Google Maps permission to collect our data however permissions are not enough let's take another app for example say Pokemon go it also supposedly has legitimate reasons to collect our location data and it also integrates with several third-party libraries for example unity3d for graphics rendering Facebook for providing like and share buttons and advertising libraries and once you've given location permissions to Pokemon go all the libraries contained within it
{"source": "grass-yt-cc-by.-VprET0tpB4|shard_10024_31|create_multimodal_data.extract_yt_and_generate_transcripts.52|shard_10024|create_multimodal_data.generate_captions.43|shard_10024|create_multimodal_data.generate_captions.57", "params": {"id": "-VprET0tpB4", "channel_id": "UC-m6oi7a-8LffTk64J3tq-w", "duration": 1193, "view_count": 162, "age_limit": 0, "chapters": [{"start_time": 0.0, "title": "Intro", "end_time": 23.0}, {"start_time": 23.0, "title": "Motivation", "end_time": 63.0}, {"start_time": 63.0, "title": "Permissions are not Enough", "end_time": 140.0}, {"start_time": 140.0, "title": "Related Work: Network Monitoring", "end_time": 176.0}, {"start_time": 176.0, "title": "Related Work: Blocking A&T", "end_time": 466.0}, {"start_time": 466.0, "title": "Building NoMOATS", "end_time": 530.0}, {"start_time": 530.0, "title": "Background: Android Network APIs", "end_time": 605.0}, {"start_time": 605.0, "title": "Hooking into Network APIs", "end_time": 684.0}, {"start_time": 684.0, "title": "Automatically Exploring Apps", "end_time": 696.0}, {"start_time": 696.0, "title": "Example Output", "end_time": 711.0}, {"start_time": 711.0, "title": "Automatic Packet Labeling", "end_time": 778.0}, {"start_time": 778.0, "title": "Training Classifiers: General", "end_time": 833.0}, {"start_time": 833.0, "title": "Training Classifiers: Per-App", "end_time": 893.0}, {"start_time": 893.0, "title": "Training Classifiers: Prediction Time", "end_time": 913.0}, {"start_time": 913.0, "title": "Evaluation: Approach", "end_time": 1099.0}, {"start_time": 1099.0, "title": "Evaluation: General Classifier", "end_time": 1169.0}, {"start_time": 1169.0, "title": "Future Directions", "end_time": 1193}], "like_count": 5, "channel_follower_count": 934, "license": "Creative Commons Attribution license (reuse allowed)", "uploader_id": "@petsymposium", "yt_tar_file": "/leonardo_work/EUHPC_E03_068/audio/fixed_download/grass-yt-cc-by//shard_10024/-VprET0tpB4.tar.gz"}}