Personal data collected at scale from surveys or digital devices offers
important insights for statistical analysis and scientific research. Safely
sharing such data while protecting privacy is however challenging.
Anonymization allows data to be shared while minimizing privacy risks, but
traditional anonymization techniques have been repeatedly shown to provide
limited protection against re-identification attacks in practice. Among modern
anonymization techniques, synthetic data generation (SDG) has emerged as a
potential solution to find a good tradeoff between privacy and statistical
utility. Synthetic data is typically generated using algorithms that learn the
statistical distribution of the original records, to then generate “artificial”
records that are structurally and statistically similar to the original ones.
Yet, the fact that synthetic records are “artificial” does not, per se,
guarantee that privacy is protected. In this work, we systematically evaluate
the tradeoffs between protecting privacy and preserving statistical utility for
a wide range of synthetic data generation algorithms. Modeling privacy as
protection against attribute inference attacks (AIAs), we extend and adapt
linear reconstruction attacks, which have not been previously studied in the
context of synthetic data. While prior work suggests that AIAs may be effective
only on few outlier records, we show they can be very effective even on
randomly selected records. We evaluate attacks on synthetic datasets ranging
from 10^3 to 10^6 records, showing that even for the same generative model, the
attack effectiveness can drastically increase when a larger number of synthetic
records is generated. Overall, our findings prove that synthetic data is
subject to privacy-utility tradeoffs just like other anonymization techniques:
when good utility is preserved, attribute inference can be a risk for many data

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