![]() Efficient scans enable you to build a strong foundation for your overall data privacy and security. The crucial first step in compliance is to understand what constitutes sensitive data, where and how it is stored, and who can access it. This makes it easy for your organization to uncover and close privacy gaps, prioritize remediation, and make informed decisions about third-party data sharing and privacy concerns before a digital transformation implementation. Complete visibility and controlĪ centralized console with rich visualizations and detailed reports offers a clear view of sensitive data and its risks. The detailed reporting supports compliance programs and facilitates executive communication. A streamlined workflow exposes security blind spots and reduces remediation time. ![]() Supporting both agentless and agent-based deployment models, the solution provides built-in templates that enable rapid identification of regulated data, highlight security risks, and help you uncover compliance gaps. Thales CipherTrust Data Discovery and Classification efficiently identifies structured as well as unstructured sensitive data on-premises and in the cloud. ![]() As a proof-of-concept, we integrate our protocols in the client application of the open-source messenger Signal.Effective solution for enterprise-wide data privacy The online phase of our fastest protocol takes only 2.92s measured on a real WiFi connection (6.53s on LTE) to check 1024 client contacts against a large-scale database with $2^$ entries. Compared to previous smartphone implementations, this yields a performance improvement of factor 1000x for circuit evaluations. Furthermore, we implement both protocols with security against malicious clients in C/C and utilize the ARM Cryptography Extensions available in most recent smartphones. In a protocol performing oblivious PRF evaluations via garbled circuits, we replace AES as the evaluated PRF with a variant of LowMC (Albrecht et al., EUROCRYPT'15) for which we determine optimal parameters, thereby reducing the communication by factor 8.2x. (PoPETS'17) while also allowing for malicious clients.Ĭoncretely, we present novel precomputation techniques for correlated oblivious transfers (reducing the online communication by factor 2x), Cuckoo filter compression (with a compression ratio of $\approx 70\%$), as well as 4.3x smaller Cuckoo filter updates. In our work, we remove most obstacles for large-scale global deployment by significantly improving two promising protocols by Kiss et al. This is due to their high computation and/or communication complexity as well as lacking optimization for mobile devices. Unfortunately, even in a weak security model where clients are assumed to follow the protocol honestly, previous protocols and implementations turned out to be far from practical when used at scale. The most promising approaches addressing this problem revolve around private set intersection (PSI) protocols. As we find, even messengers with privacy in mind currently do not deploy proper mechanisms to perform contact discovery privately. However, such a procedure poses significant privacy risks and legal challenges. ![]() This allows the service provider to determine which of the user's contacts are registered to the messaging service. Mobile messengers like WhatsApp perform contact discovery by uploading the user's entire address book to the service provider. Paper 2019/517 Mobile Private Contact Discovery at Scaleĭaniel Kales, Christian Rechberger, Thomas Schneider, Matthias Senker, and Christian Weinert ![]()
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