  Sequential recommendation has attracted a lot of attention from both academia
and industry, however the privacy risks associated to gathering and
transferring users' personal interaction data are often underestimated or
ignored. Existing privacy-preserving studies are mainly applied to traditional
collaborative filtering or matrix factorization rather than sequential
recommendation. Moreover, these studies are mostly based on differential
privacy or federated learning, which often leads to significant performance
degradation, or has high requirements for communication. In this work, we
address privacy-preserving from a different perspective. Unlike existing
research, we capture collaborative signals of neighbor interaction sequences
and directly inject indistinguishable items into the target sequence before the
recommendation process begins, thereby increasing the perplexity of the target
sequence. Even if the target interaction sequence is obtained by attackers, it
is difficult to discern which ones are the actual user interaction records. To
achieve this goal, we propose a CoLlaborative-cOnfusion seqUential recommenDer,
namely CLOUD, which incorporates a collaborative confusion mechanism to edit
the raw interaction sequences before conducting recommendation. Specifically,
CLOUD first calculates the similarity between the target interaction sequence
and other neighbor sequences to find similar sequences. Then, CLOUD considers
the shared representation of the target sequence and similar sequences to
determine the operation to be performed: keep, delete, or insert. We design a
copy mechanism to make items from similar sequences have a higher probability
to be inserted into the target sequence. Finally, the modified sequence is used
to train the recommender and predict the next item.
