Secure Range-Searching Using Copy-And-Recurse

Eyal Kushnir, Guy Moshkowich, Hayim Shaul
{"title":"Secure Range-Searching Using Copy-And-Recurse","authors":"Eyal Kushnir, Guy Moshkowich, Hayim Shaul","doi":"10.56553/popets-2024-0096","DOIUrl":null,"url":null,"abstract":"Range searching is the problem of preprocessing a set of points P, such that given a query range gamma we can efficiently compute some function f(P cap gamma). For example, in a 1 dimensional range counting query, P is a set of numbers, gamma is a segment and we need to count how many numbers of P are in gamma. In higher dimensions, P is a set of d dimensional points and the query range is some volume in R^d. In general, we want to compute more than just counting, for example, the average of P cap gamma. Range searching has applications in databases where some SELECT queries can be translated to range queries. It had received a lot of attention in computational geometry where a data structure called partition tree was shown to solve range queries in time sub-linear in |P| using space only linear in |P|. In this paper we consider partition trees under FHE where we answer range queries without learning the value of the points or the parameters of the range. We show how partition trees can be securely traversed with O(t n^{1-1/d+epsilon} + n^{1+epsilon}) operations, where n=|P|, t is the number of operations needed to compare to gamma and epsilon>0 is a parameter. When the ranges are axis-parallel hyper-boxes the running time is O(t n^epsilon + n log^{d-1} n). As far as we know, this is the first non-trivial bound on range searching under FHE and it improves over the naive solution that needs O(t n) operations. Our algorithms are independent of the encryption scheme but as an example we implemented them using the CKKS FHE scheme. Our experiments show that for databases of sizes 2^{23} and 2^{25}, our algorithms run x2.8 and x4.7 (respectively) faster than the naive algorithm. The improvement of our algorithm comes from a method we call copy-and-recurse. With it we efficiently traverse a r-ary tree (where each inner node has r children) that also has the property that at most xi of them need to be recursed into when traversing the tree. We believe this method is interesting in its own and can be used to improve traversals in other tree-like structures.","PeriodicalId":13158,"journal":{"name":"IACR Cryptol. ePrint Arch.","volume":"5 20","pages":"983"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IACR Cryptol. ePrint Arch.","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.56553/popets-2024-0096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Range searching is the problem of preprocessing a set of points P, such that given a query range gamma we can efficiently compute some function f(P cap gamma). For example, in a 1 dimensional range counting query, P is a set of numbers, gamma is a segment and we need to count how many numbers of P are in gamma. In higher dimensions, P is a set of d dimensional points and the query range is some volume in R^d. In general, we want to compute more than just counting, for example, the average of P cap gamma. Range searching has applications in databases where some SELECT queries can be translated to range queries. It had received a lot of attention in computational geometry where a data structure called partition tree was shown to solve range queries in time sub-linear in |P| using space only linear in |P|. In this paper we consider partition trees under FHE where we answer range queries without learning the value of the points or the parameters of the range. We show how partition trees can be securely traversed with O(t n^{1-1/d+epsilon} + n^{1+epsilon}) operations, where n=|P|, t is the number of operations needed to compare to gamma and epsilon>0 is a parameter. When the ranges are axis-parallel hyper-boxes the running time is O(t n^epsilon + n log^{d-1} n). As far as we know, this is the first non-trivial bound on range searching under FHE and it improves over the naive solution that needs O(t n) operations. Our algorithms are independent of the encryption scheme but as an example we implemented them using the CKKS FHE scheme. Our experiments show that for databases of sizes 2^{23} and 2^{25}, our algorithms run x2.8 and x4.7 (respectively) faster than the naive algorithm. The improvement of our algorithm comes from a method we call copy-and-recurse. With it we efficiently traverse a r-ary tree (where each inner node has r children) that also has the property that at most xi of them need to be recursed into when traversing the tree. We believe this method is interesting in its own and can be used to improve traversals in other tree-like structures.
使用复制和递归功能进行安全范围搜索
范围搜索是对一组点 P 进行预处理的问题,在给定查询范围 gamma 的情况下,我们可以高效地计算出某个函数 f(P-cap-gamma)。例如,在一维范围计数查询中,P 是一组数字,gamma 是一个分段,我们需要计算 P 中有多少数字在 gamma 中。在更高维度中,P 是一组 d 维点,查询范围是 R^d 中的某个体积。一般来说,我们需要计算的不仅仅是计数,例如 P cap gamma 的平均值。范围搜索在数据库中也有应用,一些 SELECT 查询可以转化为范围查询。它在计算几何中受到了广泛关注,一种名为分区树的数据结构被证明可以在时间与 |P| 成亚线性关系的情况下,使用空间与 |P| 成线性关系的情况下,解决范围查询问题。在本文中,我们考虑了 FHE 下的分区树,在这种情况下,我们可以在不了解点的值或范围参数的情况下回答范围查询。我们展示了如何用 O(t n^{1-1/d+epsilon} + n^{1+epsilon})操作安全地遍历分区树,其中 n=|P|,t 是与 gamma 比较所需的操作次数,epsilon>0 是一个参数。当范围是轴平行超方框时,运行时间为 O(t n^epsilon + n log^{d-1} n)。据我们所知,这是 FHE 下范围搜索的第一个非微观约束,它比需要 O(t n) 次操作的天真解决方案有所改进。我们的算法与加密方案无关,但作为示例,我们使用 CKKS FHE 方案实现了这些算法。实验结果表明,对于大小为 2^{23} 和 2^{25} 的数据库,我们的算法比传统算法分别快 x2.8 和 x4.7。我们算法的改进来自于一种我们称之为复制和递归的方法。通过这种方法,我们可以高效地遍历一棵 rary 树(每个内部节点都有 r 个子节点),而且在遍历这棵树时,最多需要遍历 xi 个子节点。我们认为这种方法本身就很有趣,而且可以用来改进其他树状结构的遍历。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信