![]() ![]() Please visit the migration section of our frequently asked questions page to learn more. We will need your AWS account ID and Wickr Pro network ID to connect your AWS and Wickr accounts. Q: How do I migrate my Wickr Pro account to AWS Wickr?ĪWS Wickr network admins can fill out this form to submit a request to migrate their existing Wickr Pro networks to AWS Wickr. If you migrate your Wickr Pro account to AWS Wickr, your data will remain intact and be transferred to your AWS Wickr network. We will not be able to give you access to your data once Wickr Pro is discontinued. ![]() Please choose the Operating System below suited to your device below. Use these links to download Wickr Me, Wickr Pro, or Wickr Enterprise. ![]() You must download files to your device prior to January 31, 2024. Wickr is the most secure collaboration platform in the world. In addition, all networks, messages, and files will be deleted and no longer accessible on January 31, 2024. On January 31, 2024, administrators and users will no longer be able to log in to their Wickr Admin Console or Wickr Pro app. Q: What happens to my Wickr Pro account if I don’t migrate by January 31, 2024? Unlike Signal, which uses your phone number, Wickr doesnt require a phone. To get started with AWS Wickr, click here. Unlike other video conferencing and collaboration platforms, Wickr Pro is. With the announcement of the general availability of AWS Wickr, we are no longer offering new-user registrations on Wickr Pro. Our main technical contribution is the construction is a family of DAGs on n nodes with constant indegree with high “sustained-space complexity”, meaning that any parallel black-pebbling strategy requires Ω(n/log(n) pebbles for at least Ω(n) steps.Īlong the way we construct a family of maximally “depth-robust” DAGs with maximum indegree O(n/log(n)) O(log(n)), improving upon the construction of Mahmoody et al.Q: Why can’t I sign up for a Wickr Pro account? We construct functions (in the parallel random oracle model) whose sustained-memory complexity is almost optimal: our function can be evaluated using n steps and O(n/log(n)) memory, in each step making one query to the (fixed-input length) random oracle, while any algorithm that can make arbitrary many parallel queries to the random oracle, still needs Ω(n/log(n) memory for Ω(n) steps.Īs has been done for various notions (including cmc) before, we reduce the task of constructing an MHFs with high sustained-memory complexity to proving pebbling lower bounds on DAGs. ![]() In this work we address this problem, and introduce the notion of sustained-memory complexity, which requires that any algorithm evaluating the function must use a large amount of memory for many steps. Still, cmc has been criticized as insufficient, as it fails to capture possible time-memory trade-offs as memory cost doesn’t scale linearly, functions with the same cmc could still have very different actual hardware cost. Unlike previous notions, cmc takes into account that dedicated hardware might exploit amortization and parallelism. They advocate that a good MHF must have high cmc. MHFs have interesting cryptographic applications, most notably to password hashing and securing blockchains.Īlwen and Serbinenko define the cumulative memory complexity (cmc) of a function as the sum (over all time-steps) of the amount of memory required to compute the function. MHFs are egalitarian, in the sense that evaluating them on dedicated hardware (like FPGAs or ASICs) is not much cheaper than on off-the-shelf hardware (like x86 CPUs). Memory-hard functions (MHF) are functions whose evaluation cost is dominated by memory cost. Authors: Joël Alwen, Jeremiah Blocki, Krzysztof Pietrzak ![]()
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