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Safety, liveness, and privacy are three critical properties for any private proof-of-stake (PoS) blockchain. However, prior work (SP'21) has shown that to obtain safety and liveness, a PoS blockchain must in theory forgo privacy. In particular, to obtain safety and liveness, PoS blockchains elect parties proportional to their stake, which, in turn, can potentially reveal the stake of a party even if the transaction processing mechanism is private. In this work, we make two key contributions. First, we present the first stake inference attack that can be actually run in practice. Specifically, our attack applies to both deterministic and randomized PoS protocols and has exponentially lesser running time in comparison with the SOTA approach. Second, we use differentially private stake distortion to achieve privacy in PoS blockchains. We formulate certain privacy requirements to achieve transaction and stake privacy, and design two stake distortion mechanisms that any PoS protocol can use. Moreover, we analyze our proposed mechanisms with Ethereum 2.0, a well-known PoS blockchain that is already operating in practice. The results indicate that our mechanisms mitigate stake inference risks and, at the same time, provide reasonable privacy while preserving required safety and liveness properties. 

Safety, liveness, and privacy are three critical properties for any private proof-of-stake (PoS) blockchain. However, prior work (SP'21) has shown that to obtain safety and liveness, a PoS blockchain must, in theory, forgo privacy. In particular, to obtain safety and liveness, PoS blockchains elect parties proportional to their stake, which, in turn, can potentially reveal the stake of a party even if the transaction processing mechanism is private. In this work, we make two key contributions. First, we present the first stake inference attack that can be actually run in practice. Specifically, our attack applies to both deterministic and randomized PoS protocols and has exponentially lesser running time in comparison with the SOTA approach. Second, we use differentially private stake distortion to achieve privacy in PoS blockchains. We formulate certain privacy requirements to achieve transaction and stake privacy, and design two stake distortion mechanisms that any PoS protocol can use. Moreover, we analyze our proposed mechanisms with Ethereum 2.0, a well-known PoS blockchain that is already operating in practice. The results indicate that our mechanisms mitigate stake inference risks and, at the same time, provide reasonable privacy while preserving required safety and liveness properties. 

In this paper, we consider secure outsourced growing databases (SOGDB) that support view-based query answering. These databases allow untrusted servers to privately maintain a materialized view. This allows servers to use only the materialized view for query processing instead of accessing the original data from which the view was derived. To tackle this, we devise a novel view-based SOGDB framework, Incshrink. The key features of this solution are: (i) Incshrink maintains the view using incremental MPC operators which eliminates the need for a trusted third party upfront, and (ii) to ensure high performance, Incshrink guarantees that the leakage satisfies DP in the presence of updates. To the best of our knowledge, there are no existing systems that have these properties. We demonstrate Incshrink's practical feasibility in terms of efficiency and accuracy with extensive experiments on real-world datasets and the TPC-ds benchmark. The evaluation results show that Incshrink provides a 3-way trade-off in terms of privacy, accuracy and efficiency, and offers at least a 7,800x performance advantage over standard SOGDB that do not support view-based query paradigm. 

In this paper, we consider privacy-preserving update strategies for secure outsourced growing databases. Such databases allow appendonly data updates on the outsourced data structure while analysis is ongoing. Despite a plethora of solutions to securely outsource database computation, existing techniques do not consider the information that can be leaked via update patterns. To address this problem, we design a novel secure outsourced database framework for growing data, DP-Sync, which interoperate with a large class of existing encrypted databases and supports efficient updates while providing differentially-private guarantees for any single update. We demonstrate DP-Sync's practical feasibility in terms of performance and accuracy with extensive empirical evaluations on real world datasets. 

The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularity, especially on mobile devices. To address the challenge, we envision that the weight pruning technique will help DNNs against MIA while reducing model storage and computational operation. In this work, we propose a pruning algorithm, and we show that the proposed algorithm can find a subnetwork that can prevent privacy leakage from MIA and achieves competitive accuracy with the original DNNs. We also verify our theoretical insights with experiments. Our experimental results illustrate that the attack accuracy using model compression is up to 13.6% and 10% lower than that of the baseline and Min-Max game, accordingly.