An existing StudentLife Study mobile dataset was evaluated and organized to be applied to different machine learning methods. Different variables like user activity, exercise, sleep, study space, social, and stress levels are optimized to train a model that could predict user stress level. The different machine learning methods would test if both patient data privacy and training efficiency can be ensured

Nguyen, Matthew

English

Old Dominion University

Old Dominion University ODU Digital Commons Cybersecurity Undergraduate Research 2021 Fall Cybersecurity Undergraduate Research Projects Protection of Patient Privacy on Mobile Device Machine Learning Matthew Nguyen Old Dominion University Follow this and additional works at: https://digitalcommons.odu.edu/covacci-undergraduateresearch  Part of the Artificial Intelligence and Robotics Commons, Digital Communications and Networking Commons, and the Information Security Commons Nguyen, Matthew, "Protection of Patient Privacy on Mobile Device Machine Learning" (2021). Cybersecurity Undergraduate Research. 9. https://digitalcommons.odu.edu/covacci-undergraduateresearch/2021fall/projects/9 This Paper is brought to you for free and open access by the Undergraduate Student Events at ODU Digital Commons. It has been accepted for inclusion in Cybersecurity Undergraduate Research by an authorized administrator of ODU Digital Commons. For more information, please contact digitalcommons@odu.edu. 1                Protection  of  Patient  Privacy  on  Mobile  Device  Machine  Learning  Matthew  Nguyen  Jiangwen  Sun  (Mentor)  Old  Dominion  University  November  22,  2021                                                2  Abstract-  An  existing  StudentLife  Study  mobile  dataset  was  evaluated  and  organized  to  be  applied  to  different  machine  learning  methods.  Different  variables  like  user  activity,  exercise,  sleep,  study  space,  social,  and  stress  levels  are  optimized  to  train  a  model  that  could  predict  user  stress  level.  The  different  machine  learning  methods  would  test  if  both  patient  data  privacy  and  training  efficiency  can  be  ensured.     Keywords-  centralized  machine  learning,  decentralized  machine  learning,  federated  learning,  federated  multi-task  learning      Ⅰ.  INTRODUCTION    The  concerns  for  patient  data  privacy  on  mobile  devices  have  become  increasingly  discussed  as  healthcare  transitions  towards  a  technologically  based  system.  Data  is  constantly  being  collected  and  stored  on  our  mobile  devices  by  applications  and  websites.  However,  many  patients  who  have  their  private  data  stored  on  their  mobile  devices  wish  to  keep  their  information  secure  without  being  shared  or  accessed  by  others.  As  the  growth  of  mobile  technology  progresses  towards  the  field  of  artificial  intelligence,  machine  learning,  and  deep  learning,  the  use  of  patient  data  is  crucial  in  the  development  of  such  technologies.  More  importantly,  mobile  patient  data  needs  to  be  kept  private  and  secure  without  compromising  the  advancement  of  new  technology.  In  this  work,  we  explored  different  methods  of  machine  learning  to  apply  to  the  StudentLife  Study  mobile  dataset  from  Dartmouth  College  to  predict  user  stress  levels.  The  dataset  was  first  sorted  and  analyzed  to  see  the  desired  data  points  and  dataset  variables  we  wanted  to  work  with.  We  intended  to  use  the  variables  of  activity,  exercise,  sleep,  study  space,  and  social  level,  to  predict  and  compare  it  to  user  stress  levels.  The  different  methods  of  machine  learning  include  potentially  looking  at  centralized  machine  learning,  decentralized  federated  learning,  and  decentralized  multi-federated  learning.  Our  task  was  to  analyze  the  results  presented  from  each  learning  method  and  understand  which  machine  learning  method  gave  us  the  most  accurate  stress  level  while  maintaining  mobile  patient  data  privacy.     3  Ⅱ.  StudentLife  Study  Mobile  Dataset    The  dataset  used  from  a  Dartmouth  study  provided  us  with  both  passive  and  automatic  sensing  information  from  the  phones  of  48  undergraduate  and  graduate  students  over  the  span  of  10  weeks  that  comprises  53  GB  continuously  collected  data,  32,000  self-report  data  points,  and  pre/post  surveys.  The  StudentLife  app  used  on  user  mobile  devices  during  this  study  has  collected  the  following  data  points  on  the  users  involved:    ● objective  sensing  data  including  sleep  (bedtime,  duration,  wake  up),  conservation  duration,  conversation  frequency,  and  physical  activity  (stationary,  walk,  run)  ● location-based  data  of  location,  co-location,  indoor  and  outdoor  mobility  ● Self-reports  with  affect  (PAM),  stress,  behavior,  Boston  bombing  reaction,  cancelled classes,  class  opinion,  comment,  Dartmouth  now,  Dimension  incident,  Dimension  protest,  dining  halls,  events,  exercise,  Green  Key,  lab,  mood,  loneliness,  social  and  study  spaces.  ● academic  performance  data:  class  information,  deadlines,  grades  (grades,  term  GPA,  cumulative  GPA),  piazza  data  ● dining  data  with  meals  data,  location  and  time  ● seating  data  with  seating  position  of  students  in  Android  programming    The  large  dataset  was  downloaded  and  evaluated  using  HPC  (High  Performance  Computing).  The  dataset  was  then  transferred  from  a  json  text  file  into  a  readable  table  using  the  Matlab  and  Microsoft  Excel  application.  We  consolidated  the  dataset  into  the  following  variables  intended  to  be  used  for  the  machine  learning  application:    Dataset  variables  to  measure  stress  levels  Description  Activity  Location,  alone  working,  alone  relaxing,  with  other  people  working,  and  with  other  people  relaxing.  Exercise  Location,  if  they  have/not  exercised,  if  they 4    A  total  of  15  users  were  found  to  have  data  available  for  activity,  exercise,  sleep,  study  space,  social,  and  stress,  which  had  sufficient  data  available  for  training  machine  learning  models.  For  users  to  qualify  to  be  used  in  training  a  model,  they  must  include  entries  for  all  necessary  variables  that  exceed  a  total  of  10  data  entries.  All  variable  data  points  had  a  response  time  along  with  each  entry.  The  data  entries  were  organized  in  chronological  order  based  on  response  time  associated  with  the  entry.   could/not  exercise  because  of  schedule,  the  length  of  time  for  exercise,  and  length  of  time  for  walking.  Sleep  Location,  hours  slept  the  previous  night,  rating  sleep  quality,  and  trouble  staying  awake.  Study  Space  Location,  place  of  study,  rate  of  productivity,  and  ambient  noise  level    Social  Location,  the  number  of  people  they  interacted  with   Stress  Outcome  (Binary  variable  based  on  response)  (  0  =  no  stress,  1  =  stress)  5    Fig.  1.  The  pie  chart  shows  the  percentage  of  stress  levels  of  all  15  users  throughout  the  10  week  study  period.  Users  were  stressed  for  around  76%  of  the  time  and  not  stressed  for  about  24%  of  the  time  during  the  study.      Fig.  2.  The  bar  graph  shows  the  percent  of  the  activity  being  accomplished  by  all  15  users  throughout  the  10  week  study  period.  The  majority  of  time  during  this  study  was  spent  working  alone  with  a  normal  distribution  representation.  Relaxing  with  others,  working  with  others,  and  relaxing  alone  skews  to  the  right  towards  the  0-10%  category,  which  indicates  that  those  activities  were  done  the  least.  6    Fig.  3.  The  pie  chart  shows  the  entry  responses  asking  if  daily  vigorous  exercise  was  done  by  all  15  users  throughout  the  10  week  study  period.  The  majority  of  entries  submitted  by  users  stated  that  they  did  not  participate  in  vigorous  exercise  that  day.      Fig.  4.  The  pie  chart  shows  the  percentage  of  minutes  of  exercise  done  by  all  15  users  throughout  the  10  week  study  period.  The  majority  of  entries  submitted  by  users  stated  that  they  participated  in  0  minutes  of  exercise.  For  the  users  who  did  exercise,  14%  of  them  spent  roughly  30  minutes  to  1  hour  exercising.  7    Fig.  5.  The  bar  graph  shows  the  number  of  entries  of  hours  of  sleep  recorded  by  all  15  users  throughout  the  10  week  study  period.  This  shows  a  majority  of  users  had  around  7  to  8  hours  of  sleep.     Fig.  6.  The  pie  chart  shows  the  percent  of  daily  exhaustion  experienced  by  all  15  users  throughout  the  10  week  study  period  in  correlation  with  their  sleep.  This  shows  that  users  were  primarily  not  exhausted  and  about  20%  of  the  time  felt  exhausted  once. 8    Fig.  7.  The  pie  chart  shows  the  percentage  of  sleep  rating  given  by  all  15  users  throughout  the  10  week  study  period  in  correlation  to  their  sleep.  This  shows  a  majority  felt  that  they  had  very  good  or  fairly  good  sleep  based  on  the  hours  of  sleep  they  received.      Fig.  8.  The  pie  chart  shows  the  percentage  of  productivity  accomplished  by  all  15  users  throughout  the  10  week  study  period  in  their  designated  study  space.  From  the  entries  submitted  by  users,  43%  of  the  time  spent  studying  was  mildly  productive  while  roughly  20%  of  the  time  studying  was  super  productive,  super  distracte,  or  mildly  distracted.  9      Fig.  9.  The  pie  chart  shows  the  percentage  of  ambient  noise  level  recorded  by  all  15  users  throughout  the  10  week  study  period  in  their  designated  study  space.  From  the  entries  submitted  by  users,  52%  of  the  time  spent  studying  was  in  a  silent  space  while  27%  of  the  time  studying  had  white  noise.    Fig.  10.  The  bar  graph  shows  the  daily  number  of  people  interacted  with  by  all  15  users  throughout  the  10  week  study.  The  majority  of  days  where  users  had  social  interaction  involves  primarily  encountering  a  number  of  5  to  49  people.   10  Ⅲ.  Machine  Learning  Methods    Machine  learning  traditionally  involves  training  a  model  in  a  centralized  method  where  individual  device  information  is  stored  over  a  shared  cloud  database  for  Internet  of  Things  (IoT).  The  individualized  data  is  then  trained  to  produce  a  broad  model  which  gets  redistributed  and  used  by  all  the  devices.  Despite  the  convenience  behind  creating  a  general  model  that  can  be  broadly  applied,  there  are  many  issues  associated  with  the  methodology.  Many  concerns  involve  the  amount  of  battery  consumption  centralized  training  incurs.  Transferring  data  to  a  centralized  cloud  server  and  downloading  the  model  updates  continuously  can  quickly  drain  the  device  battery  life  and  be  slow  for  devices  with  an  unstable  network  connection.  An  additional  concern  with  centralized  training  is  the  lack  of  user  data  privacy.  Data  sent  to  the  cloud  can  be  at  risk  for  being  misused  or  damaged.      Fig.  11.   A  diagram  of  centralized  machine  learning.  Each  device  sends  data  directly  to  the  cloud  server.  A  generalized  model  is  generated  and  sent  to  all  devices  to  be  applied.  11      Decentralized  machine  learning  is  a  more  privatized  and  newer  approach  to  training  a  machine  learning  model.  Decentralized  machine  learning  prevents  personal  data  from  being  exposed  to  the  cloud  server  by  creating  a  user  specific  model  using  the  streaming  data  on  each  device.  The  individually  trained  models  are  then  sent  to  the  cloud  server  to  be  compared  for  variations  and  sent  back  to  each  device  to  be  improved  on  over  time.  Decentralized  machine  learning  learns  from  data  stored  locally  on  each  device  which  keeps  user  data  confidential  and  safe  from  the  cloud.  The  amount  of  battery  consumption  is  lowered  since  only  trained  models  are  sent  to  the  cloud  opposed  to  all  user  data.  Keeping  data  localized  and  only  sending  models  to  the  cloud  decreases  the  dependency  for  constant  internet  connection.     A  more  specialized,  decentralized  machine  learning  method  that  allows  for  a  general  training  model  to  be  created  is  called  federated  learning.  Federated  learning  allows  for  a  single  model  to  be  trained  and  used  among  all  devices  while  maintaining  the  privacy  and  efficiency  of  decentralized  learning.  It  operates  by  distributing  an  initial  model  to  all  devices  that  trains  and  enhances  the  model  with  local  user  data.  The  locally  trained  models  are  then  collected  in  the  cloud  to  be  averaged  into  a  new  generalized  model  that  is  received  by  each  device  to  continue  the  learning  cycle.  The  application  of  federated  learning  for  mobile  machine  learning  has  been  simplified  with  the  open-source  framework  called  TensorFlow  Federated  (TFF)  made  by  Google.  This  framework  can  be  installed  with  a  pip  package  manager  and  has  the  option  for  developers  to  choose  from  existing  available  models.  12    Fig.  12.   A  diagram  of  decentralized  federated  machine  learning.  An  initial  model  is  sent  to  all  devices  and  trained  locally  by  the  TensorFlow  Federated  application  using  local  device  data.  A  locally  modified  model  is  then  produced  and  received  by  the  cloud  server.  All  models  are  averaged  to  produce  a  new  single  model  to  be  learned  by  all  devices.    The  disadvantage  with  applying  a  generalized  learning  model  to  all  user  data  is  that  it  might  give  inaccurate  results  when  trying  to  measure  personalized  metrics  like  mood,  behavior,  or  habits.  A  more  user  specific  learning  approach  is  federated  multi-task  learning.  This  approach factors  in  the  varying  heterogeneity  in  each  client  by  learning  a  personalized  model  for  each  client.  Individualized  user  models  are  first  learned  using  client  data.  An  improved  personal  model  is  then  received  by  a  cloud  server  for  model  relationship  learning  and  sent  back  to  the  client  to  continue  the  learning  cycle.   13    Fig.  13.   A  diagram  of  decentralized  federated  multi-task  machine  learning.  Each  device  locally  trains  an  individual  model  through  the  TensorFlow  Federated  application.  The  device  model  parameters  are  sent  to  the  cloud.  A  model  relationship  is  learned  and  distributed  to  devices  to  improve  personalized  model  learning.    Ⅳ.  CONCLUSION     For  the  duration  of  this  research,  our  goal  was  to  understand  how  patient  data  can  be  protected  alongside  the  application  of  mobile  machine  learning.  We  wanted  to  explore  this  by  using  the  existing  StudentLife  Study  Mobile  Dataset.  We  used  High  Performance  Computing  (HPC),  Matlab,  and  Excel  to  organize  the  data  points  for  15  users.  Each  user  had  sufficient  data  for  the  variables  of  activity,  exercise,  sleep,  study  space,  and  stress.  These  variables  are  used  to  train  a  model  predicting  user  stress  levels.  We  would  then  compare  the  resulting  stress  levels  14  predicted  by  centralized  machine  learning,  decentralized  federated  learning,  and  decentralized  federated  multi-task  learning.  Due  to  time  constraints,  we  did  not  have  the  opportunity  to  apply  the  user  data  to  train  models  for  the  three  different  learning  methods.  The  project  hopes  to  be  continued  to  further  our  understanding  of  how  patient  data  can  be  protected  in  the  machine  learning  process.     REFERENCES  Arun.  (2020,  October  6).  Understanding  federated  learning .  Medium.  Retrieved  November  19,  2021,  from  https://towardsdatascience.com/understanding-federated-learning-99bc86a0d026.   Federated  multi-task  learning  under  a  mixture  ...  -  arxiv.org .  (n.d.).  Retrieved  November  19,  2021,  from  https://arxiv.org/pdf/2108.10252.   Liu1,  J.  C.,  Goetz1,  J.,  Sen2,  S.,  Tewari1,  A.,  Statistics,  1D.  of,  &  Liu,  C.  A.  J.  C.  (n.d.).  Learning  from  others  without  sacrificing  privacy:  Simulation  comparing  centralized  and  Federated  Machine  Learning  on  Mobile  Health  Data .  JMIR  mHealth  and  uHealth.  Retrieved  November  19,  2021,  from  https://mhealth.jmir.org/2021/3/e23728.   On  multiplicative  multitask  feature  learning .  (n.d.).  Retrieved  November  19,  2021,  from  https://cs.odu.edu/~jsun/doc/pub/nips-wang-2014.pdf.   SWP  federated  learning  final  version  -  ekkono  solutions  ab .  (n.d.).  Retrieved  November  19,  2021,  from  https://www.ekkono.ai/wp-content/uploads/2020/12/SWP_Federated_Learning_Ekkono_ Solutions_May_2020.pdf.   

Protection of Patient Privacy on Mobile Device Machine Learning

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Protection of Patient Privacy on Mobile Device Machine Learning

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