Secure Personal Memory-Sharing with Co-located People and Places

The abundance of interconnected devices in the Internet of
Things (IoT) offers a powerful vision on how automated capture
systems can aid humans remember their lives better. Already
today, mobile and wearable devices allow people to create
rich logs of their daily experiences in the form of photos,
videos, GPS traces, or even physiological data. This activity is
often called “lifelogging”, and has led to the so-called “quantified
self” movement where people capture detailed traces of
their everyday lives in order to better understand themselves.
An interesting avenue to explore in this context is the possibility
of capturing lifelog data for the sake of augmenting
one’s memory. Contemporary psychology theory suggests that
captured experiences of daily events can be used to generate
cues (hints) which, when reviewed, can improve subsequent
long-term recall of these memories. However, limitations of
on-body placement of wearable devices can yield poor quality
data and restricts capture to a first-person perspective. The
focus of this work is to enable the secure and automatic exchange
of one’s lifelog streams with both co-located peers
and any available capture devices in an IoT infrastructure,
in order to construct a more comprehensive representation
of a previous experience, which can thus help one to create
more effective cues. We present a privacy-aware architecture
for this exchange and report on a proof-of-concept prototype
implementation.