  Data cleaning is a pervasive problem for organizations as they try to reap
value from their data. Recent advances in networking and cloud computing
technology have fueled a new computing paradigm called Database-as-a-Service,
where data management tasks are outsourced to large service providers. In this
paper, we consider a Data Cleaning-as-a-Service model that allows a client to
interact with a data cleaning provider who hosts curated, and sensitive data.
We present PACAS: a Privacy-Aware data Cleaning-As-a-Service model that
facilitates interaction between the parties with client query requests for
data, and a service provider using a data pricing scheme that computes prices
according to data sensitivity. We propose new extensions to the model to define
generalized data repairs that obfuscate sensitive data to allow data sharing
between the client and service provider. We present a new semantic distance
measure to quantify the utility of such repairs, and we re-define the notion of
consistency in the presence of generalized values. The PACAS model uses
(X,Y,L)-anonymity that extends existing data publishing techniques to consider
the semantics in the data while protecting sensitive values. Our evaluation
over real data show that PACAS safeguards semantically related sensitive
values, and provides lower repair errors compared to existing privacy-aware
cleaning techniques.
