<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Privacy Preserved Mobile Sensing Using Region-Based Group Signature."
Abstract:"Mobile sensing receives lots of attention since it exploits sensors embedded in smartphones to gather information for quality improvement of daily life. In particular, mobile users (MUs) carrying smartphones act as queriers who request information provided by MUs who act as data collectors, and the data collection and delivery are facilitated by human mobility and ubiquity. However, additional privacy considerations rise in this paradigm, where MUs might not want to reveal their identities when providing some information at a specific position during a particular period. By adopting group signature, where any member of its group can sign a message on behalf of the entire group, the signer of the sensed information cannot be recognized, and thus, anonymity is preserved. The concept of group signature can be realized when introducing mobile edge computing, where base stations are clustered into a region to serve a group of MUs. In such region-based group signature, MUs located in the same region are grouped and thus are indistinguishable. Due to the localized movement feature of human, MUs might not frequently leave the current group and join a new group, which subsequently reduces the infamous rekeying overhead. We implement group signature on Android smartphones and conduct simulation experiments to investigate the performance of the proposed region-based grouping approach from the perspectives of rekeying overhead and delay. The simulation results show that the region-based grouping method is efficient. Moreover, the optimal group number for the minimization of rekeying overhead is suggested.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
Q2 = [];
Q3 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Privacy Preserved Mobile Sensing Using Region-Based Group Signature
Mobile sensing receives lots of attention since it exploits sensors embedded in smartphones to gather information for quality improvement of daily life. In particular, mobile users (MUs) carrying smartphones act as queriers who request information provided by MUs who act as data collectors, and the data collection and delivery are facilitated by human mobility and ubiquity. However, additional privacy considerations rise in this paradigm, where MUs might not want to reveal their identities when providing some information at a specific position during a particular period. By adopting group signature, where any member of its group can sign a message on behalf of the entire group, the signer of the sensed information cannot be recognized, and thus, anonymity is preserved. The concept of group signature can be realized when introducing mobile edge computing, where base stations are clustered into a region to serve a group of MUs. In such region-based group signature, MUs located in the same region are grouped and thus are indistinguishable. Due to the localized movement feature of human, MUs might not frequently leave the current group and join a new group, which subsequently reduces the infamous rekeying overhead. We implement group signature on Android smartphones and conduct simulation experiments to investigate the performance of the proposed region-based grouping approach from the perspectives of rekeying overhead and delay. The simulation results show that the region-based grouping method is efficient. Moreover, the optimal group number for the minimization of rekeying overhead is suggested.