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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.