from mikecb’s Activity on Github: https://github.com/google/gitprotocolio
Sherlock’s statement is most often quoted to imply that uncommon
scenarios can all be explained away by reason and logic. This is missing
the point. The quote’s power is in the elimination of the impossible
before engaging in such reasoning. The present authors seek to expose
a similar misapplication of methodology as it exists throughout information
security and offer a framework by which to elevate the common Watson.
from mikecb’s Activity on Github: https://github.com/bryant/argon2rs
from mikecb’s Activity on Github: https://github.com/google/JWS
from mikecb’s Activity on Github: https://github.com/fastly/ftw
from mikecb’s Activity on Github: https://github.com/redmed666/mal6raph
The large-scale monitoring of computer users’ software
activities has become commonplace, e.g., for application
telemetry, error reporting, or demographic profiling. This
paper describes a principled systems architecture—Encode,
Shuffle, Analyze (ESA)—for performing such monitoring
with high utility while also protecting user privacy. The ESA
design, and its PROCHLO implementation, are informed by
our practical experiences with an existing, large deployment
of privacy-preserving software monitoring.
With ESA, the privacy of monitored users’ data is guaranteed
by its processing in a three-step pipeline. First, the data
is encoded to control scope, granularity, and randomness.
Second, the encoded data is collected in batches subject to
a randomized threshold, and blindly shuffled, to break linkability
and to ensure that individual data items get “lost in the
crowd” of the batch. Third, the anonymous, shuffled data is
analyzed by a specific analysis engine that further prevents
statistical inference attacks on analysis results.
ESA extends existing best-practice methods for sensitivedata
analytics, by using cryptography and statistical techniques
to make explicit how data is elided and reduced in
precision, how only common-enough, anonymous data is analyzed,
and how this is done for only specific, permitted purposes.
As a result, ESA remains compatible with the established
workflows of traditional database analysis.
Strong privacy guarantees, including differential privacy,
can be established at each processing step to defend
against malice or compromise at one or more of those steps.
PROCHLO develops new techniques to harden those steps,
including the Stash Shuffle, a novel scalable and efficient
oblivious-shuffling algorithm based on Intel’s SGX, and new
applications of cryptographic secret sharing and blinding.
We describe ESA and PROCHLO, as well as experiments
that validate their ability to balance utility and privacy.