Collaboration between humans and machines does not necessarily lead to better outcomes.

Data sets containing private information about individuals must satisfy privacy standards before being publicly released. One such standard, k-anonymity, reduces the probability of the re-identification of individuals by requiring that rare combinations of personally-identifiable information be represented by at least k distinct individuals. Records that violate this standard must be altered, which can lead to significant distortion of the statistical properties of the data set. In this paper, we discuss improvements to two techniques used to achieve k-anonymity, generalization and suppression, that confer k-anonymity while better preserving the statistical properties of an educational data set taken from a massive online open course platform, edX.

We argue that objects that interact in a distributed system need to be dealt with in ways that are intrinsically different from objects that interact in a single address space. These differences are required because distributed systems require that the programmer be aware of latency, have a different model of memory access, and take into account issues of concurrency and partial failure.

We look at a number of distributed systems that have attempted to paper over the distinction between local and remote objects, and show that such systems fail to support basic requirements of robustness and reliability. These failures have been masked in the past by the small size of the distributed systems that have been built. In the enterprise-wide distributed systems foreseen in the near future, however, such a masking will be impossible.
We conclude by discussing what is required of both systems-level and application-level programmers and designers if one is to take distribution seriously.