Oblivious Random Access Machine (ORAM) is a kind of cryptographic primitive that allows a client to access its private data from the server without disclosing the access pattern. To deal with consecutive requested blocks at a time efficiently, range ORAM (rORAM) is presented. In the previous rORAM scheme, the locality, namely, the number of discontinuous seeks to complete a request, is reduced to O(log2 N), nevertheless, the bandwidth cost is increased to the poly-logarithmic level. Hence, there exists an open question, that is, whether rORAM can be constructed with the same bandwidth efficiency as a regular ORAM, that is, O(log N)-block? In this paper, we propose a new rORAM scheme, called L2-rORAM. In our scheme, a compatible superblock technique is proposed, and it is combined together with an eviction technique for range blocks, so that it avoids duplication of multiple copies and extra dummy access. As a result, it obtains O(log N)-block bandwidth cost, which affirmatively answers the above open question. Meanwhile, the data locality is reduced to O(log N). In addition, the client storage is maintained at the small level of O(log N)-block, and the server storage is maintained at the unexpanded level of O(N)-block. Finally, experimental results show that the average response time of our L2-rORAM is reduced by one order of magnitude over the state-of-the-art rORAM scheme.

Graph neural networks (GNN) have been widely used in many machine learning tasks, such as text classification, sequence labeling, protein interface prediction, and knowledge graph. With increasing security concerns, GNN have been proved to be vulnerable and unreliable. Recent years, inspired by adversarial model in computer vision, various attacks on graph data begin to emerge. However, the exist attacks mostly focus on security violation and attack specificity, and very few attacks concern error specificity. In this paper, we focus on dealing with this kind of attack on one node of the graph by slightly manipulating the graph structure. Our goal is to change the label of the node to what we want after attack. We formulate this case as label specificity attack problem. The biggest challenge in solving this problem is the lack of theoretical guidance to perform this attack. For this, we reinterpret structural entropy and define differential structural entropy (DS-entropy) to guide the manipulation. Based on DS-entropy, we propose the target-label principle and max-degree principle to execute our attack, and then design the corresponding algorithm DSEM. We compare our algorithm DSEM with two benchmarks on three classical graph data sets. Results show that our algorithm is effective in performing label specificity attacks.

Dynamic searchable encryption (SE) aims at achieving varied search function over encrypted database in dynamic setting, which is a trade-off in efficiency, security, and functionality. Recent work proposes a file-injection attack which can successfully attack by utilizing some information leaked in the update process. To mitigate this attack, some SE schemes with forward privacy are proposed. However, these schemes are designed to achieve single keyword or conjunctive keyword search, which cannot support multikeyword search. Moreover, these schemes do not consider the function of results ranking. In this paper, we propose a forward privacy multikeyword ranked search scheme over encrypted database. We design a forward privacy multikeyword search scheme based on the classic MRSE scheme. Our scheme makes the cloud cannot obtain the actual match results of the past query with the newly updated files by adding the well-chosen dummy elements to the original index and query vectors. We rank the search results based on the matched keyword number and the T F × I D F $TF\times IDF$ rule in the dynamic setting. Our scheme uses only the symmetric encryption primitive. We implement our scheme for COVID-19 data set and the experimental evaluation results show that the proposed scheme is secure and efficient.

Ridge regression (RR) is an important machine learning technique which introduces a regularization hyperparameter$\alpha$to ordinary multiple linear regression for analyzing data suffering from multicollinearity. In this paper, we present a quantum algorithm for RR, where the technique of parallel Hamiltonian simulation to simulate a number of Hermitian matrices in parallel is proposed and used to develop a quantum version of$K$-fold cross-validation approach, which can efficiently estimate the predictive performance of RR. Our algorithm consists of two phases: (1) using quantum$K$-fold cross-validation to efficiently determine a good$\alpha$with which RR can achieve good predictive performance, and then (2) generating a quantum state encoding the optimal fitting parameters of RR with such$\alpha$, which can be further utilized to predict new data. Since indefinite dense Hamiltonian simulation has been adopted as a key subroutine, our algorithm can efficiently handle non-sparse data matrices. It is shown that our algorithm can achieve exponential speedup over the classical counterpart for (low-rank) data matrices with low condition numbers. But when the condition numbers of data matrices are large to be amenable to full or approximately full ranks of data matrices, only polynomial speedup can be achieved.