Many online services continuously collect users' private data for real-time analytics. In the current ecosystem of data analytics, the analysts usually have direct access to users' private data and must be trusted not to abuse it. A pragmatic ecosystem has two desirable, but contradictory design requirements. Users seek stronger privacy, while analysts strive for high-utility analytics in real time. Much of this data arrives as a data stream and in huge volumes, requiring real-time stream processing based on distributed systems. New computing paradigms try to address these concerns. On the one hand, privacy-preserving analytics protects the user data by local storage of information and integration of noise, while on the other hand, approximate computation enables lowlatency, high-throughput stream analytics. However, the respective research fields address the issue separately. State-of-the-art privacy-preserving systems typically operate on single-shot batch queries and are, therefore, not applicable to real-time stream processing. Approximate computation proposals build on the underlying assumption of a centralized dataset. Thus, both solutions are not compatible for trivial integration. Today, sources of privacy-relevant data streams are pervasive and can be found almost everywhere. IoT devices, automotives, mobile phones, wearables, or workstations, all stream privacy-related information. This includes, but is not limited to, environmental measurements, health data, location for navigation, augmented reality, or social contacts, as well as statistical information about the usage of devices. We concentrate our analysis on location information, more specifically on the distances traveled using taxis based on the NYC taxi ride dataset and power consumption of households represented by the Household Electricity Consumption dataset. Inference upon this information can easily reveal working practices, habits, personal preferences, social connections, medical issues, and other highly personal information. It is therefore of utmost importance to preserve the privacy of the users involved. To guarantee privacy of users within a stream analytic ecosystem, we suggest a system that combines the results of the computing paradigms mentioned. Indeed, we make the observation that privacypreserving analytics and approximate computation are complementary. Both paradigms strive for an
Lastly, PrivApprox REF supports privacy-preserving data analytics using a combination of randomized response and approximate computation.